Overview

Dataset statistics

Number of variables65
Number of observations46
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory226.5 KiB
Average record size in memory4.9 KiB

Variable types

CAT53
NUM8
BOOL4

Warnings

ingles_conversa is highly correlated with nomeHigh correlation
nome is highly correlated with ingles_conversa and 9 other fieldsHigh correlation
curso is highly correlated with id_candidatoHigh correlation
id_candidato is highly correlated with curso and 3 other fieldsHigh correlation
previsao_formacao is highly correlated with nomeHigh correlation
tool_spark is highly correlated with nomeHigh correlation
tool_docker is highly correlated with nomeHigh correlation
tool_keras is highly correlated with id_candidato and 1 other fieldsHigh correlation
tool_c++ is highly correlated with id_candidatoHigh correlation
tool_C# is highly correlated with nomeHigh correlation
tool_plotly is highly correlated with nomeHigh correlation
algoritmo_aprend_nao_super is highly correlated with nomeHigh correlation
algoritmo_visao_computacional is highly correlated with nomeHigh correlation
algoritmo_pdi is highly correlated with nomeHigh correlation
algoritmo_robotica is highly correlated with id_candidatoHigh correlation
df_index has unique values Unique
id_candidato has unique values Unique
nome has unique values Unique

Reproduction

Analysis started2020-12-02 18:18:47.428784
Analysis finished2020-12-02 18:19:24.871854
Duration37.44 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.93478261
Minimum1
Maximum51
Zeros0
Zeros (%)0.0%
Memory size496.0 B
2020-12-02T15:19:24.950036image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.5
Q114.25
median26
Q337.75
95-th percentile48.5
Maximum51
Range50
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation14.44745602
Coefficient of variation (CV)0.557068715
Kurtosis-1.115723425
Mean25.93478261
Median Absolute Deviation (MAD)12
Skewness-0.009624384148
Sum1193
Variance208.7289855
MonotocityStrictly increasing
2020-12-02T15:19:25.122427image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%) 
5112.2%
 
1412.2%
 
2312.2%
 
2212.2%
 
2112.2%
 
2012.2%
 
1912.2%
 
1812.2%
 
1712.2%
 
1612.2%
 
1512.2%
 
1312.2%
 
5012.2%
 
1212.2%
 
1112.2%
 
1012.2%
 
812.2%
 
712.2%
 
612.2%
 
512.2%
 
312.2%
 
212.2%
 
2412.2%
 
2512.2%
 
2712.2%
 
Other values (21)2145.7%
 
ValueCountFrequency (%) 
112.2%
 
212.2%
 
312.2%
 
512.2%
 
612.2%
 
712.2%
 
812.2%
 
1012.2%
 
1112.2%
 
1212.2%
 
ValueCountFrequency (%) 
5112.2%
 
5012.2%
 
4912.2%
 
4712.2%
 
4512.2%
 
4412.2%
 
4312.2%
 
4212.2%
 
4112.2%
 
4012.2%
 

id_candidato
Categorical

HIGH CORRELATION
UNIQUE

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size496.0 B
25
 
1
43
 
1
20
 
1
47
 
1
7
 
1
Other values (41)
41 
ValueCountFrequency (%) 
2512.2%
 
4312.2%
 
2012.2%
 
4712.2%
 
712.2%
 
1812.2%
 
4112.2%
 
1712.2%
 
2912.2%
 
2712.2%
 
4512.2%
 
2412.2%
 
3512.2%
 
3612.2%
 
5012.2%
 
3712.2%
 
4212.2%
 
512.2%
 
5112.2%
 
2812.2%
 
212.2%
 
1412.2%
 
4912.2%
 
1112.2%
 
612.2%
 
Other values (21)2145.7%
 
2020-12-02T15:19:25.298812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique46 ?
Unique (%)100.0%
2020-12-02T15:19:25.452428image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.847826087
Min length1

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
11618.8%
 
31517.6%
 
21416.5%
 
41214.1%
 
578.2%
 
755.9%
 
055.9%
 
844.7%
 
944.7%
 
633.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number85100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
11618.8%
 
31517.6%
 
21416.5%
 
41214.1%
 
578.2%
 
755.9%
 
055.9%
 
844.7%
 
944.7%
 
633.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common85100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
11618.8%
 
31517.6%
 
21416.5%
 
41214.1%
 
578.2%
 
755.9%
 
055.9%
 
844.7%
 
944.7%
 
633.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII85100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
11618.8%
 
31517.6%
 
21416.5%
 
41214.1%
 
578.2%
 
755.9%
 
055.9%
 
844.7%
 
944.7%
 
633.5%
 

nome
Categorical

HIGH CORRELATION
UNIQUE

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size496.0 B
Davos Seaworth
 
1
Jaime Lannister
 
1
Jorah Mormont
 
1
Olenna Tyrell
 
1
Walder Frey
 
1
Other values (41)
41 
ValueCountFrequency (%) 
Davos Seaworth12.2%
 
Jaime Lannister12.2%
 
Jorah Mormont12.2%
 
Olenna Tyrell12.2%
 
Walder Frey12.2%
 
Jon Snow12.2%
 
Theon Greyjoy12.2%
 
Stannis Baratheon12.2%
 
Samwell Tarly12.2%
 
Margaery Tyrell12.2%
 
Rhaegar Targaryen12.2%
 
Podrick Payne12.2%
 
Tommen Baratheon12.2%
 
Lysa Arryn12.2%
 
Doran Martell12.2%
 
Asha Greyjoy12.2%
 
Balon Greyjoy12.2%
 
Eddard Ned Stark12.2%
 
Cersei Lannister12.2%
 
Euron Greyjoy12.2%
 
Robert Baratheon12.2%
 
Robert Arryn12.2%
 
Sansa Stark12.2%
 
Tywin Lannister12.2%
 
Sandor Clegane12.2%
 
Other values (21)2145.7%
 
2020-12-02T15:19:25.629853image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique46 ?
Unique (%)100.0%
2020-12-02T15:19:25.773083image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length13
Mean length13.2826087
Min length5

Overview of Unicode Properties

Unique unicode characters43
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
r7011.5%
 
a6811.1%
 
e609.8%
 
n498.0%
 
457.4%
 
o396.4%
 
y304.9%
 
l294.7%
 
t274.4%
 
h172.8%
 
i172.8%
 
s152.5%
 
S122.0%
 
B111.8%
 
T101.6%
 
d91.5%
 
g91.5%
 
M81.3%
 
m81.3%
 
k61.0%
 
J61.0%
 
R61.0%
 
G50.8%
 
C50.8%
 
b50.8%
 
Other values (18)457.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter47577.7%
 
Uppercase Letter9114.9%
 
Space Separator457.4%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S1213.2%
 
B1112.1%
 
T1011.0%
 
M88.8%
 
J66.6%
 
R66.6%
 
G55.5%
 
C55.5%
 
A44.4%
 
L44.4%
 
D44.4%
 
E44.4%
 
P33.3%
 
N22.2%
 
O22.2%
 
V22.2%
 
K11.1%
 
W11.1%
 
F11.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
r7014.7%
 
a6814.3%
 
e6012.6%
 
n4910.3%
 
o398.2%
 
y306.3%
 
l296.1%
 
t275.7%
 
h173.6%
 
i173.6%
 
s153.2%
 
d91.9%
 
g91.9%
 
m81.7%
 
k61.3%
 
b51.1%
 
j40.8%
 
w40.8%
 
u30.6%
 
f20.4%
 
c20.4%
 
v10.2%
 
z10.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
45100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin56692.6%
 
Common457.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
r7012.4%
 
a6812.0%
 
e6010.6%
 
n498.7%
 
o396.9%
 
y305.3%
 
l295.1%
 
t274.8%
 
h173.0%
 
i173.0%
 
s152.7%
 
S122.1%
 
B111.9%
 
T101.8%
 
d91.6%
 
g91.6%
 
M81.4%
 
m81.4%
 
k61.1%
 
J61.1%
 
R61.1%
 
G50.9%
 
C50.9%
 
b50.9%
 
A40.7%
 
Other values (17)417.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
45100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII611100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
r7011.5%
 
a6811.1%
 
e609.8%
 
n498.0%
 
457.4%
 
o396.4%
 
y304.9%
 
l294.7%
 
t274.4%
 
h172.8%
 
i172.8%
 
s152.5%
 
S122.0%
 
B111.8%
 
T101.6%
 
d91.5%
 
g91.5%
 
M81.3%
 
m81.3%
 
k61.0%
 
J61.0%
 
R61.0%
 
G50.8%
 
C50.8%
 
b50.8%
 
Other values (18)457.4%
 

idade
Real number (ℝ≥0)

Distinct14
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.23913043
Minimum19
Maximum39
Zeros0
Zeros (%)0.0%
Memory size312.0 B
2020-12-02T15:19:25.910259image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile20
Q123
median24
Q325.75
95-th percentile38.25
Maximum39
Range20
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation5.275455884
Coefficient of variation (CV)0.2090189239
Kurtosis1.630199258
Mean25.23913043
Median Absolute Deviation (MAD)1.5
Skewness1.558007286
Sum1161
Variance27.83043478
MonotocityNot monotonic
2020-12-02T15:19:26.047202image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%) 
231123.9%
 
24919.6%
 
3936.5%
 
2536.5%
 
2236.5%
 
2136.5%
 
2036.5%
 
3524.3%
 
2724.3%
 
2624.3%
 
1924.3%
 
3612.2%
 
3112.2%
 
3012.2%
 
ValueCountFrequency (%) 
1924.3%
 
2036.5%
 
2136.5%
 
2236.5%
 
231123.9%
 
24919.6%
 
2536.5%
 
2624.3%
 
2724.3%
 
3012.2%
 
ValueCountFrequency (%) 
3936.5%
 
3612.2%
 
3524.3%
 
3112.2%
 
3012.2%
 
2724.3%
 
2624.3%
 
2536.5%
 
24919.6%
 
231123.9%
 
Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size496.0 B
True
41 
False
ValueCountFrequency (%) 
True4189.1%
 
False510.9%
 
2020-12-02T15:19:26.158815image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size496.0 B
True
39 
False
ValueCountFrequency (%) 
True3984.8%
 
False715.2%
 
2020-12-02T15:19:26.206023image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

horas_estagio
Categorical

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size496.0 B
30h
39 
20h
ValueCountFrequency (%) 
30h3984.8%
 
20h715.2%
 
2020-12-02T15:19:26.294513image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:26.388367image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:26.472690image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
04633.3%
 
h4633.3%
 
33928.3%
 
275.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number9266.7%
 
Lowercase Letter4633.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
04650.0%
 
33942.4%
 
277.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
h46100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common9266.7%
 
Latin4633.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
04650.0%
 
33942.4%
 
277.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
h46100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII138100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
04633.3%
 
h4633.3%
 
33928.3%
 
275.1%
 

turno_estagio
Categorical

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size496.0 B
full
31 
restrito
15 
ValueCountFrequency (%) 
full3167.4%
 
restrito1532.6%
 
2020-12-02T15:19:26.608077image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:26.707519image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:26.804199image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length4
Mean length5.304347826
Min length4

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
l6225.4%
 
f3112.7%
 
u3112.7%
 
r3012.3%
 
t3012.3%
 
e156.1%
 
s156.1%
 
i156.1%
 
o156.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter244100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
l6225.4%
 
f3112.7%
 
u3112.7%
 
r3012.3%
 
t3012.3%
 
e156.1%
 
s156.1%
 
i156.1%
 
o156.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin244100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
l6225.4%
 
f3112.7%
 
u3112.7%
 
r3012.3%
 
t3012.3%
 
e156.1%
 
s156.1%
 
i156.1%
 
o156.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII244100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
l6225.4%
 
f3112.7%
 
u3112.7%
 
r3012.3%
 
t3012.3%
 
e156.1%
 
s156.1%
 
i156.1%
 
o156.1%
 

ingles_leitura
Categorical

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
F
16 
A
13 
I
13 
B
ValueCountFrequency (%) 
F1634.8%
 
A1328.3%
 
I1328.3%
 
B48.7%
 
2020-12-02T15:19:26.962736image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:27.069496image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:27.193843image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
F1634.8%
 
A1328.3%
 
I1328.3%
 
B48.7%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter46100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
F1634.8%
 
A1328.3%
 
I1328.3%
 
B48.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin46100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
F1634.8%
 
A1328.3%
 
I1328.3%
 
B48.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII46100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
F1634.8%
 
A1328.3%
 
I1328.3%
 
B48.7%
 

ingles_escrita
Categorical

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
I
19 
A
11 
B
F
ValueCountFrequency (%) 
I1941.3%
 
A1123.9%
 
B919.6%
 
F715.2%
 
2020-12-02T15:19:27.374146image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:27.481781image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:27.573306image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
I1941.3%
 
A1123.9%
 
B919.6%
 
F715.2%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter46100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
I1941.3%
 
A1123.9%
 
B919.6%
 
F715.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin46100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
I1941.3%
 
A1123.9%
 
B919.6%
 
F715.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII46100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
I1941.3%
 
A1123.9%
 
B919.6%
 
F715.2%
 

ingles_conversa
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
B
13 
I
13 
A
12 
F
ValueCountFrequency (%) 
B1328.3%
 
I1328.3%
 
A1226.1%
 
F817.4%
 
2020-12-02T15:19:27.924786image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:28.017647image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:28.118703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
B1328.3%
 
I1328.3%
 
A1226.1%
 
F817.4%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter46100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
B1328.3%
 
I1328.3%
 
A1226.1%
 
F817.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin46100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
B1328.3%
 
I1328.3%
 
A1226.1%
 
F817.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII46100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
B1328.3%
 
I1328.3%
 
A1226.1%
 
F817.4%
 
Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
0meses
32 
12meses
6meses
>12meses
 
3
ValueCountFrequency (%) 
0meses3269.6%
 
12meses715.2%
 
6meses48.7%
 
>12meses36.5%
 
2020-12-02T15:19:28.263715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:28.370960image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:28.491358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length6
Mean length6.282608696
Min length6

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e9231.8%
 
s9231.8%
 
m4615.9%
 
03211.1%
 
1103.5%
 
2103.5%
 
641.4%
 
>31.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter23079.6%
 
Decimal Number5619.4%
 
Math Symbol31.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
03257.1%
 
11017.9%
 
21017.9%
 
647.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e9240.0%
 
s9240.0%
 
m4620.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
>3100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin23079.6%
 
Common5920.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
03254.2%
 
11016.9%
 
21016.9%
 
646.8%
 
>35.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e9240.0%
 
s9240.0%
 
m4620.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII289100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e9231.8%
 
s9231.8%
 
m4615.9%
 
03211.1%
 
1103.5%
 
2103.5%
 
641.4%
 
>31.0%
 

titulacao
Categorical

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size496.0 B
graduacao
45 
pos
 
1
ValueCountFrequency (%) 
graduacao4597.8%
 
pos12.2%
 
2020-12-02T15:19:28.645689image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)2.2%
2020-12-02T15:19:28.737673image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:28.832631image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length9
Mean length8.869565217
Min length3

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a13533.1%
 
o4611.3%
 
g4511.0%
 
r4511.0%
 
d4511.0%
 
u4511.0%
 
c4511.0%
 
p10.2%
 
s10.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter408100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a13533.1%
 
o4611.3%
 
g4511.0%
 
r4511.0%
 
d4511.0%
 
u4511.0%
 
c4511.0%
 
p10.2%
 
s10.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin408100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a13533.1%
 
o4611.3%
 
g4511.0%
 
r4511.0%
 
d4511.0%
 
u4511.0%
 
c4511.0%
 
p10.2%
 
s10.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII408100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a13533.1%
 
o4611.3%
 
g4511.0%
 
r4511.0%
 
d4511.0%
 
u4511.0%
 
c4511.0%
 
p10.2%
 
s10.2%
 

curso
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size496.0 B
A
24 
B
19 
C
ValueCountFrequency (%) 
A2452.2%
 
B1941.3%
 
C36.5%
 
2020-12-02T15:19:28.994924image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:29.109264image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:29.224336image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A2452.2%
 
B1941.3%
 
C36.5%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter46100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A2452.2%
 
B1941.3%
 
C36.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin46100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A2452.2%
 
B1941.3%
 
C36.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII46100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A2452.2%
 
B1941.3%
 
C36.5%
 
Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size496.0 B
True
36 
False
10 
ValueCountFrequency (%) 
True3678.3%
 
False1021.7%
 
2020-12-02T15:19:29.321204image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

previsao_formacao
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size496.0 B
2022/2
13 
2022/1
11 
2021/2
2021/1
2020/2
ValueCountFrequency (%) 
2022/21328.3%
 
2022/11123.9%
 
2021/2919.6%
 
2021/1715.2%
 
2020/248.7%
 
>2022/224.3%
 
2020-12-02T15:19:29.428204image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:29.531806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:29.656510image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length6
Mean length6.043478261
Min length6

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories3 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
214652.5%
 
05018.0%
 
/4616.5%
 
13412.2%
 
>20.7%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number23082.7%
 
Other Punctuation4616.5%
 
Math Symbol20.7%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
>2100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
214663.5%
 
05021.7%
 
13414.8%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/46100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common278100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
214652.5%
 
05018.0%
 
/4616.5%
 
13412.2%
 
>20.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII278100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
214652.5%
 
05018.0%
 
/4616.5%
 
13412.2%
 
>20.7%
 

progresso_curso
Categorical

Distinct3
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size496.0 B
2/3
27 
1/3
12 
0/3
ValueCountFrequency (%) 
2/32758.7%
 
1/31226.1%
 
0/3715.2%
 
2020-12-02T15:19:29.808096image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:29.920548image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:30.018870image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
/4633.3%
 
34633.3%
 
22719.6%
 
1128.7%
 
075.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number9266.7%
 
Other Punctuation4633.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
34650.0%
 
22729.3%
 
11213.0%
 
077.6%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/46100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common138100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
/4633.3%
 
34633.3%
 
22719.6%
 
1128.7%
 
075.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII138100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
/4633.3%
 
34633.3%
 
22719.6%
 
1128.7%
 
075.1%
 

estagio_DS
Categorical

Distinct3
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size496.0 B
0
40 
1
 
4
>1
 
2
ValueCountFrequency (%) 
04087.0%
 
148.7%
 
>124.3%
 
2020-12-02T15:19:30.163247image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:30.284929image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:30.414609image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.043478261
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
04083.3%
 
1612.5%
 
>24.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number4695.8%
 
Math Symbol24.2%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
04087.0%
 
1613.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
>2100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common48100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
04083.3%
 
1612.5%
 
>24.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII48100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
04083.3%
 
1612.5%
 
>24.2%
 

IC
Categorical

Distinct3
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size496.0 B
Não
36 
Concluido
Fazendo
 
2
ValueCountFrequency (%) 
Não3678.3%
 
Concluido817.4%
 
Fazendo24.3%
 
2020-12-02T15:19:30.617743image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:30.720295image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:30.828573image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length3
Mean length4.217391304
Min length3

Overview of Unicode Properties

Unique unicode characters14
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o5427.8%
 
N3618.6%
 
ã3618.6%
 
n105.2%
 
d105.2%
 
C84.1%
 
c84.1%
 
l84.1%
 
u84.1%
 
i84.1%
 
F21.0%
 
a21.0%
 
z21.0%
 
e21.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter14876.3%
 
Uppercase Letter4623.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N3678.3%
 
C817.4%
 
F24.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o5436.5%
 
ã3624.3%
 
n106.8%
 
d106.8%
 
c85.4%
 
l85.4%
 
u85.4%
 
i85.4%
 
a21.4%
 
z21.4%
 
e21.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin194100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o5427.8%
 
N3618.6%
 
ã3618.6%
 
n105.2%
 
d105.2%
 
C84.1%
 
c84.1%
 
l84.1%
 
u84.1%
 
i84.1%
 
F21.0%
 
a21.0%
 
z21.0%
 
e21.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII15881.4%
 
None3618.6%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o5434.2%
 
N3622.8%
 
n106.3%
 
d106.3%
 
C85.1%
 
c85.1%
 
l85.1%
 
u85.1%
 
i85.1%
 
F21.3%
 
a21.3%
 
z21.3%
 
e21.3%
 

Most frequent None characters

ValueCountFrequency (%) 
ã36100.0%
 

TCC
Categorical

Distinct3
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size496.0 B
Não
34 
Fazendo
Concluido
ValueCountFrequency (%) 
Não3473.9%
 
Fazendo817.4%
 
Concluido48.7%
 
2020-12-02T15:19:31.013084image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:31.142022image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:31.254297image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length3
Mean length4.217391304
Min length3

Overview of Unicode Properties

Unique unicode characters14
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o5025.8%
 
N3417.5%
 
ã3417.5%
 
n126.2%
 
d126.2%
 
F84.1%
 
a84.1%
 
z84.1%
 
e84.1%
 
C42.1%
 
c42.1%
 
l42.1%
 
u42.1%
 
i42.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter14876.3%
 
Uppercase Letter4623.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N3473.9%
 
F817.4%
 
C48.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o5033.8%
 
ã3423.0%
 
n128.1%
 
d128.1%
 
a85.4%
 
z85.4%
 
e85.4%
 
c42.7%
 
l42.7%
 
u42.7%
 
i42.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin194100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o5025.8%
 
N3417.5%
 
ã3417.5%
 
n126.2%
 
d126.2%
 
F84.1%
 
a84.1%
 
z84.1%
 
e84.1%
 
C42.1%
 
c42.1%
 
l42.1%
 
u42.1%
 
i42.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII16082.5%
 
None3417.5%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o5031.2%
 
N3421.2%
 
n127.5%
 
d127.5%
 
F85.0%
 
a85.0%
 
z85.0%
 
e85.0%
 
C42.5%
 
c42.5%
 
l42.5%
 
u42.5%
 
i42.5%
 

Most frequent None characters

ValueCountFrequency (%) 
ã34100.0%
 

tool_python
Categorical

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Já_trabalhei
22 
Tenho_domínio
17 
Já_ouvi_Falar
Nunca_nem_vi
 
1
ValueCountFrequency (%) 
Já_trabalhei2247.8%
 
Tenho_domínio1737.0%
 
Já_ouvi_Falar613.0%
 
Nunca_nem_vi12.2%
 
2020-12-02T15:19:31.408929image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)2.2%
2020-12-02T15:19:31.500155image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:31.622642image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length12.5
Mean length12.5
Min length12

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o579.9%
 
a579.9%
 
_539.2%
 
i468.0%
 
e407.0%
 
h396.8%
 
n366.3%
 
J284.9%
 
á284.9%
 
l284.9%
 
r284.9%
 
t223.8%
 
b223.8%
 
m183.1%
 
T173.0%
 
d173.0%
 
í173.0%
 
u71.2%
 
v71.2%
 
F61.0%
 
N10.2%
 
c10.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter47081.7%
 
Connector Punctuation539.2%
 
Uppercase Letter529.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J2853.8%
 
T1732.7%
 
F611.5%
 
N11.9%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o5712.1%
 
a5712.1%
 
i469.8%
 
e408.5%
 
h398.3%
 
n367.7%
 
á286.0%
 
l286.0%
 
r286.0%
 
t224.7%
 
b224.7%
 
m183.8%
 
d173.6%
 
í173.6%
 
u71.5%
 
v71.5%
 
c10.2%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_53100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin52290.8%
 
Common539.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o5710.9%
 
a5710.9%
 
i468.8%
 
e407.7%
 
h397.5%
 
n366.9%
 
J285.4%
 
á285.4%
 
l285.4%
 
r285.4%
 
t224.2%
 
b224.2%
 
m183.4%
 
T173.3%
 
d173.3%
 
í173.3%
 
u71.3%
 
v71.3%
 
F61.1%
 
N10.2%
 
c10.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
_53100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII53092.2%
 
None457.8%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o5710.8%
 
a5710.8%
 
_5310.0%
 
i468.7%
 
e407.5%
 
h397.4%
 
n366.8%
 
J285.3%
 
l285.3%
 
r285.3%
 
t224.2%
 
b224.2%
 
m183.4%
 
T173.2%
 
d173.2%
 
u71.3%
 
v71.3%
 
F61.1%
 
N10.2%
 
c10.2%
 

Most frequent None characters

ValueCountFrequency (%) 
á2862.2%
 
í1737.8%
 

tool_R
Categorical

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Já_ouvi_Falar
27 
Já_trabalhei
13 
Nunca_nem_vi
Tenho_domínio
 
2
ValueCountFrequency (%) 
Já_ouvi_Falar2758.7%
 
Já_trabalhei1328.3%
 
Nunca_nem_vi48.7%
 
Tenho_domínio24.3%
 
2020-12-02T15:19:31.794700image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:31.888528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:31.999427image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length13
Mean length12.63043478
Min length12

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a8414.5%
 
_7713.3%
 
i467.9%
 
J406.9%
 
á406.9%
 
l406.9%
 
r406.9%
 
o335.7%
 
u315.3%
 
v315.3%
 
F274.6%
 
e193.3%
 
h152.6%
 
t132.2%
 
b132.2%
 
n122.1%
 
m61.0%
 
N40.7%
 
c40.7%
 
T20.3%
 
d20.3%
 
í20.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter43174.2%
 
Connector Punctuation7713.3%
 
Uppercase Letter7312.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J4054.8%
 
F2737.0%
 
N45.5%
 
T22.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a8419.5%
 
i4610.7%
 
á409.3%
 
l409.3%
 
r409.3%
 
o337.7%
 
u317.2%
 
v317.2%
 
e194.4%
 
h153.5%
 
t133.0%
 
b133.0%
 
n122.8%
 
m61.4%
 
c40.9%
 
d20.5%
 
í20.5%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_77100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin50486.7%
 
Common7713.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a8416.7%
 
i469.1%
 
J407.9%
 
á407.9%
 
l407.9%
 
r407.9%
 
o336.5%
 
u316.2%
 
v316.2%
 
F275.4%
 
e193.8%
 
h153.0%
 
t132.6%
 
b132.6%
 
n122.4%
 
m61.2%
 
N40.8%
 
c40.8%
 
T20.4%
 
d20.4%
 
í20.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
_77100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII53992.8%
 
None427.2%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a8415.6%
 
_7714.3%
 
i468.5%
 
J407.4%
 
l407.4%
 
r407.4%
 
o336.1%
 
u315.8%
 
v315.8%
 
F275.0%
 
e193.5%
 
h152.8%
 
t132.4%
 
b132.4%
 
n122.2%
 
m61.1%
 
N40.7%
 
c40.7%
 
T20.4%
 
d20.4%
 

Most frequent None characters

ValueCountFrequency (%) 
á4095.2%
 
í24.8%
 

tool_MATLAB
Categorical

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Já_trabalhei
21 
Já_ouvi_Falar
15 
Nunca_nem_vi
Tenho_domínio
ValueCountFrequency (%) 
Já_trabalhei2145.7%
 
Já_ouvi_Falar1532.6%
 
Nunca_nem_vi510.9%
 
Tenho_domínio510.9%
 
2020-12-02T15:19:32.143277image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:32.257500image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:32.367193image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length12
Mean length12.43478261
Min length12

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a7713.5%
 
_6611.5%
 
i468.0%
 
J366.3%
 
á366.3%
 
r366.3%
 
l366.3%
 
e315.4%
 
o305.2%
 
h264.5%
 
t213.7%
 
b213.7%
 
u203.5%
 
n203.5%
 
v203.5%
 
F152.6%
 
m101.7%
 
N50.9%
 
c50.9%
 
T50.9%
 
d50.9%
 
í50.9%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter44577.8%
 
Connector Punctuation6611.5%
 
Uppercase Letter6110.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J3659.0%
 
F1524.6%
 
N58.2%
 
T58.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a7717.3%
 
i4610.3%
 
á368.1%
 
r368.1%
 
l368.1%
 
e317.0%
 
o306.7%
 
h265.8%
 
t214.7%
 
b214.7%
 
u204.5%
 
n204.5%
 
v204.5%
 
m102.2%
 
c51.1%
 
d51.1%
 
í51.1%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_66100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin50688.5%
 
Common6611.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a7715.2%
 
i469.1%
 
J367.1%
 
á367.1%
 
r367.1%
 
l367.1%
 
e316.1%
 
o305.9%
 
h265.1%
 
t214.2%
 
b214.2%
 
u204.0%
 
n204.0%
 
v204.0%
 
F153.0%
 
m102.0%
 
N51.0%
 
c51.0%
 
T51.0%
 
d51.0%
 
í51.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
_66100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII53192.8%
 
None417.2%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a7714.5%
 
_6612.4%
 
i468.7%
 
J366.8%
 
r366.8%
 
l366.8%
 
e315.8%
 
o305.6%
 
h264.9%
 
t214.0%
 
b214.0%
 
u203.8%
 
n203.8%
 
v203.8%
 
F152.8%
 
m101.9%
 
N50.9%
 
c50.9%
 
T50.9%
 
d50.9%
 

Most frequent None characters

ValueCountFrequency (%) 
á3687.8%
 
í512.2%
 

tool_sql
Categorical

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Já_trabalhei
21 
Já_ouvi_Falar
20 
Tenho_domínio
Nunca_nem_vi
 
1
ValueCountFrequency (%) 
Já_trabalhei2145.7%
 
Já_ouvi_Falar2043.5%
 
Tenho_domínio48.7%
 
Nunca_nem_vi12.2%
 
2020-12-02T15:19:32.527343image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)2.2%
2020-12-02T15:19:32.631698image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:32.732413image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length13
Mean length12.52173913
Min length12

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a8314.4%
 
_6711.6%
 
i468.0%
 
J417.1%
 
á417.1%
 
r417.1%
 
l417.1%
 
o325.6%
 
e264.5%
 
h254.3%
 
t213.6%
 
b213.6%
 
u213.6%
 
v213.6%
 
F203.5%
 
n101.7%
 
m50.9%
 
T40.7%
 
d40.7%
 
í40.7%
 
N10.2%
 
c10.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter44376.9%
 
Connector Punctuation6711.6%
 
Uppercase Letter6611.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J4162.1%
 
F2030.3%
 
T46.1%
 
N11.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a8318.7%
 
i4610.4%
 
á419.3%
 
r419.3%
 
l419.3%
 
o327.2%
 
e265.9%
 
h255.6%
 
t214.7%
 
b214.7%
 
u214.7%
 
v214.7%
 
n102.3%
 
m51.1%
 
d40.9%
 
í40.9%
 
c10.2%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_67100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin50988.4%
 
Common6711.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a8316.3%
 
i469.0%
 
J418.1%
 
á418.1%
 
r418.1%
 
l418.1%
 
o326.3%
 
e265.1%
 
h254.9%
 
t214.1%
 
b214.1%
 
u214.1%
 
v214.1%
 
F203.9%
 
n102.0%
 
m51.0%
 
T40.8%
 
d40.8%
 
í40.8%
 
N10.2%
 
c10.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
_67100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII53192.2%
 
None457.8%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a8315.6%
 
_6712.6%
 
i468.7%
 
J417.7%
 
r417.7%
 
l417.7%
 
o326.0%
 
e264.9%
 
h254.7%
 
t214.0%
 
b214.0%
 
u214.0%
 
v214.0%
 
F203.8%
 
n101.9%
 
m50.9%
 
T40.8%
 
d40.8%
 
N10.2%
 
c10.2%
 

Most frequent None characters

ValueCountFrequency (%) 
á4191.1%
 
í48.9%
 

tool_dw
Categorical

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Já_ouvi_Falar
25 
Nunca_nem_vi
14 
Já_trabalhei
Tenho_domínio
 
1
ValueCountFrequency (%) 
Já_ouvi_Falar2554.3%
 
Nunca_nem_vi1430.4%
 
Já_trabalhei613.0%
 
Tenho_domínio12.2%
 
2020-12-02T15:19:32.871705image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)2.2%
2020-12-02T15:19:32.971404image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:33.075658image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length13
Mean length12.56521739
Min length12

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_8514.7%
 
a7613.1%
 
i468.0%
 
u396.7%
 
v396.7%
 
J315.4%
 
á315.4%
 
l315.4%
 
r315.4%
 
n305.2%
 
o284.8%
 
F254.3%
 
e213.6%
 
m152.6%
 
N142.4%
 
c142.4%
 
h71.2%
 
t61.0%
 
b61.0%
 
T10.2%
 
d10.2%
 
í10.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter42273.0%
 
Connector Punctuation8514.7%
 
Uppercase Letter7112.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J3143.7%
 
F2535.2%
 
N1419.7%
 
T11.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a7618.0%
 
i4610.9%
 
u399.2%
 
v399.2%
 
á317.3%
 
l317.3%
 
r317.3%
 
n307.1%
 
o286.6%
 
e215.0%
 
m153.6%
 
c143.3%
 
h71.7%
 
t61.4%
 
b61.4%
 
d10.2%
 
í10.2%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_85100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin49385.3%
 
Common8514.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a7615.4%
 
i469.3%
 
u397.9%
 
v397.9%
 
J316.3%
 
á316.3%
 
l316.3%
 
r316.3%
 
n306.1%
 
o285.7%
 
F255.1%
 
e214.3%
 
m153.0%
 
N142.8%
 
c142.8%
 
h71.4%
 
t61.2%
 
b61.2%
 
T10.2%
 
d10.2%
 
í10.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
_85100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII54694.5%
 
None325.5%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_8515.6%
 
a7613.9%
 
i468.4%
 
u397.1%
 
v397.1%
 
J315.7%
 
l315.7%
 
r315.7%
 
n305.5%
 
o285.1%
 
F254.6%
 
e213.8%
 
m152.7%
 
N142.6%
 
c142.6%
 
h71.3%
 
t61.1%
 
b61.1%
 
T10.2%
 
d10.2%
 

Most frequent None characters

ValueCountFrequency (%) 
á3196.9%
 
í13.1%
 

tool_BD
Categorical

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Já_ouvi_Falar
41 
Já_trabalhei
 
3
Nunca_nem_vi
 
1
Tenho_domínio
 
1
ValueCountFrequency (%) 
Já_ouvi_Falar4189.1%
 
Já_trabalhei36.5%
 
Nunca_nem_vi12.2%
 
Tenho_domínio12.2%
 
2020-12-02T15:19:33.213147image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)4.3%
2020-12-02T15:19:33.320516image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:33.426970image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length13
Mean length12.91304348
Min length12

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a8915.0%
 
_8814.8%
 
i467.7%
 
J447.4%
 
á447.4%
 
o447.4%
 
l447.4%
 
r447.4%
 
u427.1%
 
v427.1%
 
F416.9%
 
e50.8%
 
n40.7%
 
h40.7%
 
t30.5%
 
b30.5%
 
m20.3%
 
T10.2%
 
d10.2%
 
í10.2%
 
N10.2%
 
c10.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter41970.5%
 
Connector Punctuation8814.8%
 
Uppercase Letter8714.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J4450.6%
 
F4147.1%
 
T11.1%
 
N11.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a8921.2%
 
i4611.0%
 
á4410.5%
 
o4410.5%
 
l4410.5%
 
r4410.5%
 
u4210.0%
 
v4210.0%
 
e51.2%
 
n41.0%
 
h41.0%
 
t30.7%
 
b30.7%
 
m20.5%
 
d10.2%
 
í10.2%
 
c10.2%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_88100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin50685.2%
 
Common8814.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a8917.6%
 
i469.1%
 
J448.7%
 
á448.7%
 
o448.7%
 
l448.7%
 
r448.7%
 
u428.3%
 
v428.3%
 
F418.1%
 
e51.0%
 
n40.8%
 
h40.8%
 
t30.6%
 
b30.6%
 
m20.4%
 
T10.2%
 
d10.2%
 
í10.2%
 
N10.2%
 
c10.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
_88100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII54992.4%
 
None457.6%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a8916.2%
 
_8816.0%
 
i468.4%
 
J448.0%
 
o448.0%
 
l448.0%
 
r448.0%
 
u427.7%
 
v427.7%
 
F417.5%
 
e50.9%
 
n40.7%
 
h40.7%
 
t30.5%
 
b30.5%
 
m20.4%
 
T10.2%
 
d10.2%
 
N10.2%
 
c10.2%
 

Most frequent None characters

ValueCountFrequency (%) 
á4497.8%
 
í12.2%
 

tool_cloud
Categorical

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Já_ouvi_Falar
35 
Já_trabalhei
Nunca_nem_vi
 
3
Tenho_domínio
 
1
ValueCountFrequency (%) 
Já_ouvi_Falar3576.1%
 
Já_trabalhei715.2%
 
Nunca_nem_vi36.5%
 
Tenho_domínio12.2%
 
2020-12-02T15:19:33.573720image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)2.2%
2020-12-02T15:19:33.678403image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:33.783801image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length13
Mean length12.7826087
Min length12

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a8714.8%
 
_8414.3%
 
i467.8%
 
J427.1%
 
á427.1%
 
l427.1%
 
r427.1%
 
o386.5%
 
u386.5%
 
v386.5%
 
F356.0%
 
e111.9%
 
n81.4%
 
h81.4%
 
t71.2%
 
b71.2%
 
m40.7%
 
N30.5%
 
c30.5%
 
T10.2%
 
d10.2%
 
í10.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter42371.9%
 
Connector Punctuation8414.3%
 
Uppercase Letter8113.8%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J4251.9%
 
F3543.2%
 
N33.7%
 
T11.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a8720.6%
 
i4610.9%
 
á429.9%
 
l429.9%
 
r429.9%
 
o389.0%
 
u389.0%
 
v389.0%
 
e112.6%
 
n81.9%
 
h81.9%
 
t71.7%
 
b71.7%
 
m40.9%
 
c30.7%
 
d10.2%
 
í10.2%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_84100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin50485.7%
 
Common8414.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a8717.3%
 
i469.1%
 
J428.3%
 
á428.3%
 
l428.3%
 
r428.3%
 
o387.5%
 
u387.5%
 
v387.5%
 
F356.9%
 
e112.2%
 
n81.6%
 
h81.6%
 
t71.4%
 
b71.4%
 
m40.8%
 
N30.6%
 
c30.6%
 
T10.2%
 
d10.2%
 
í10.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
_84100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII54592.7%
 
None437.3%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a8716.0%
 
_8415.4%
 
i468.4%
 
J427.7%
 
l427.7%
 
r427.7%
 
o387.0%
 
u387.0%
 
v387.0%
 
F356.4%
 
e112.0%
 
n81.5%
 
h81.5%
 
t71.3%
 
b71.3%
 
m40.7%
 
N30.6%
 
c30.6%
 
T10.2%
 
d10.2%
 

Most frequent None characters

ValueCountFrequency (%) 
á4297.7%
 
í12.3%
 

tool_scala
Categorical

Distinct3
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size496.0 B
Nunca_nem_vi
24 
Já_ouvi_Falar
21 
Já_trabalhei
 
1
ValueCountFrequency (%) 
Nunca_nem_vi2452.2%
 
Já_ouvi_Falar2145.7%
 
Já_trabalhei12.2%
 
2020-12-02T15:19:33.952652image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)2.2%
2020-12-02T15:19:34.058386image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:34.456977image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length12
Mean length12.45652174
Min length12

Overview of Unicode Properties

Unique unicode characters19
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_9115.9%
 
a6811.9%
 
n488.4%
 
i468.0%
 
u457.9%
 
v457.9%
 
e254.4%
 
N244.2%
 
c244.2%
 
m244.2%
 
J223.8%
 
á223.8%
 
l223.8%
 
r223.8%
 
o213.7%
 
F213.7%
 
t10.2%
 
b10.2%
 
h10.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter41572.4%
 
Connector Punctuation9115.9%
 
Uppercase Letter6711.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N2435.8%
 
J2232.8%
 
F2131.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a6816.4%
 
n4811.6%
 
i4611.1%
 
u4510.8%
 
v4510.8%
 
e256.0%
 
c245.8%
 
m245.8%
 
á225.3%
 
l225.3%
 
r225.3%
 
o215.1%
 
t10.2%
 
b10.2%
 
h10.2%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_91100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin48284.1%
 
Common9115.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a6814.1%
 
n4810.0%
 
i469.5%
 
u459.3%
 
v459.3%
 
e255.2%
 
N245.0%
 
c245.0%
 
m245.0%
 
J224.6%
 
á224.6%
 
l224.6%
 
r224.6%
 
o214.4%
 
F214.4%
 
t10.2%
 
b10.2%
 
h10.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
_91100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII55196.2%
 
None223.8%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_9116.5%
 
a6812.3%
 
n488.7%
 
i468.3%
 
u458.2%
 
v458.2%
 
e254.5%
 
N244.4%
 
c244.4%
 
m244.4%
 
J224.0%
 
l224.0%
 
r224.0%
 
o213.8%
 
F213.8%
 
t10.2%
 
b10.2%
 
h10.2%
 

Most frequent None characters

ValueCountFrequency (%) 
á22100.0%
 

tool_spark
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Já_ouvi_Falar
28 
Nunca_nem_vi
14 
Tenho_domínio
 
2
Já_trabalhei
 
2
ValueCountFrequency (%) 
Já_ouvi_Falar2860.9%
 
Nunca_nem_vi1430.4%
 
Tenho_domínio24.3%
 
Já_trabalhei24.3%
 
2020-12-02T15:19:34.604995image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:34.708540image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:34.816044image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length13
Mean length12.65217391
Min length12

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_8815.1%
 
a7412.7%
 
i467.9%
 
u427.2%
 
v427.2%
 
o345.8%
 
n325.5%
 
J305.2%
 
á305.2%
 
l305.2%
 
r305.2%
 
F284.8%
 
e183.1%
 
m162.7%
 
N142.4%
 
c142.4%
 
h40.7%
 
t20.3%
 
b20.3%
 
T20.3%
 
d20.3%
 
í20.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter42072.2%
 
Connector Punctuation8815.1%
 
Uppercase Letter7412.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J3040.5%
 
F2837.8%
 
N1418.9%
 
T22.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a7417.6%
 
i4611.0%
 
u4210.0%
 
v4210.0%
 
o348.1%
 
n327.6%
 
á307.1%
 
l307.1%
 
r307.1%
 
e184.3%
 
m163.8%
 
c143.3%
 
h41.0%
 
t20.5%
 
b20.5%
 
d20.5%
 
í20.5%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_88100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin49484.9%
 
Common8815.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a7415.0%
 
i469.3%
 
u428.5%
 
v428.5%
 
o346.9%
 
n326.5%
 
J306.1%
 
á306.1%
 
l306.1%
 
r306.1%
 
F285.7%
 
e183.6%
 
m163.2%
 
N142.8%
 
c142.8%
 
h40.8%
 
t20.4%
 
b20.4%
 
T20.4%
 
d20.4%
 
í20.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
_88100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII55094.5%
 
None325.5%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_8816.0%
 
a7413.5%
 
i468.4%
 
u427.6%
 
v427.6%
 
o346.2%
 
n325.8%
 
J305.5%
 
l305.5%
 
r305.5%
 
F285.1%
 
e183.3%
 
m162.9%
 
N142.5%
 
c142.5%
 
h40.7%
 
t20.4%
 
b20.4%
 
T20.4%
 
d20.4%
 

Most frequent None characters

ValueCountFrequency (%) 
á3093.8%
 
í26.2%
 

tool_net
Categorical

Distinct3
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size496.0 B
Já_ouvi_Falar
30 
Nunca_nem_vi
14 
Já_trabalhei
 
2
ValueCountFrequency (%) 
Já_ouvi_Falar3065.2%
 
Nunca_nem_vi1430.4%
 
Já_trabalhei24.3%
 
2020-12-02T15:19:34.994534image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:35.095235image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:35.193359image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length13
Mean length12.65217391
Min length12

Overview of Unicode Properties

Unique unicode characters19
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_9015.5%
 
a7813.4%
 
i467.9%
 
u447.6%
 
v447.6%
 
J325.5%
 
á325.5%
 
l325.5%
 
r325.5%
 
o305.2%
 
F305.2%
 
n284.8%
 
e162.7%
 
N142.4%
 
c142.4%
 
m142.4%
 
t20.3%
 
b20.3%
 
h20.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter41671.5%
 
Connector Punctuation9015.5%
 
Uppercase Letter7613.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J3242.1%
 
F3039.5%
 
N1418.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a7818.8%
 
i4611.1%
 
u4410.6%
 
v4410.6%
 
á327.7%
 
l327.7%
 
r327.7%
 
o307.2%
 
n286.7%
 
e163.8%
 
c143.4%
 
m143.4%
 
t20.5%
 
b20.5%
 
h20.5%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_90100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin49284.5%
 
Common9015.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a7815.9%
 
i469.3%
 
u448.9%
 
v448.9%
 
J326.5%
 
á326.5%
 
l326.5%
 
r326.5%
 
o306.1%
 
F306.1%
 
n285.7%
 
e163.3%
 
N142.8%
 
c142.8%
 
m142.8%
 
t20.4%
 
b20.4%
 
h20.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
_90100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII55094.5%
 
None325.5%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_9016.4%
 
a7814.2%
 
i468.4%
 
u448.0%
 
v448.0%
 
J325.8%
 
l325.8%
 
r325.8%
 
o305.5%
 
F305.5%
 
n285.1%
 
e162.9%
 
N142.5%
 
c142.5%
 
m142.5%
 
t20.4%
 
b20.4%
 
h20.4%
 

Most frequent None characters

ValueCountFrequency (%) 
á32100.0%
 

tool_rapidminer
Categorical

Distinct3
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size496.0 B
Nunca_nem_vi
34 
Já_ouvi_Falar
11 
Já_trabalhei
 
1
ValueCountFrequency (%) 
Nunca_nem_vi3473.9%
 
Já_ouvi_Falar1123.9%
 
Já_trabalhei12.2%
 
2020-12-02T15:19:35.386951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)2.2%
2020-12-02T15:19:35.509830image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:35.655411image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length12
Mean length12.23913043
Min length12

Overview of Unicode Properties

Unique unicode characters19
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_9116.2%
 
n6812.1%
 
a5810.3%
 
i468.2%
 
u458.0%
 
v458.0%
 
e356.2%
 
N346.0%
 
c346.0%
 
m346.0%
 
J122.1%
 
á122.1%
 
r122.1%
 
l122.1%
 
o112.0%
 
F112.0%
 
t10.2%
 
b10.2%
 
h10.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter41573.7%
 
Connector Punctuation9116.2%
 
Uppercase Letter5710.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N3459.6%
 
J1221.1%
 
F1119.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n6816.4%
 
a5814.0%
 
i4611.1%
 
u4510.8%
 
v4510.8%
 
e358.4%
 
c348.2%
 
m348.2%
 
á122.9%
 
r122.9%
 
l122.9%
 
o112.7%
 
t10.2%
 
b10.2%
 
h10.2%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_91100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin47283.8%
 
Common9116.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n6814.4%
 
a5812.3%
 
i469.7%
 
u459.5%
 
v459.5%
 
e357.4%
 
N347.2%
 
c347.2%
 
m347.2%
 
J122.5%
 
á122.5%
 
r122.5%
 
l122.5%
 
o112.3%
 
F112.3%
 
t10.2%
 
b10.2%
 
h10.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
_91100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII55197.9%
 
None122.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_9116.5%
 
n6812.3%
 
a5810.5%
 
i468.3%
 
u458.2%
 
v458.2%
 
e356.4%
 
N346.2%
 
c346.2%
 
m346.2%
 
J122.2%
 
r122.2%
 
l122.2%
 
o112.0%
 
F112.0%
 
t10.2%
 
b10.2%
 
h10.2%
 

Most frequent None characters

ValueCountFrequency (%) 
á12100.0%
 

tool_kubernets
Categorical

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size496.0 B
Nunca_nem_vi
30 
Já_ouvi_Falar
16 
ValueCountFrequency (%) 
Nunca_nem_vi3065.2%
 
Já_ouvi_Falar1634.8%
 
2020-12-02T15:19:35.839902image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:35.953666image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:36.064820image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length12
Mean length12.34782609
Min length12

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_9216.2%
 
a6210.9%
 
n6010.6%
 
u468.1%
 
v468.1%
 
i468.1%
 
N305.3%
 
c305.3%
 
e305.3%
 
m305.3%
 
J162.8%
 
á162.8%
 
o162.8%
 
F162.8%
 
l162.8%
 
r162.8%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter41472.9%
 
Connector Punctuation9216.2%
 
Uppercase Letter6210.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N3048.4%
 
J1625.8%
 
F1625.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a6215.0%
 
n6014.5%
 
u4611.1%
 
v4611.1%
 
i4611.1%
 
c307.2%
 
e307.2%
 
m307.2%
 
á163.9%
 
o163.9%
 
l163.9%
 
r163.9%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_92100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin47683.8%
 
Common9216.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a6213.0%
 
n6012.6%
 
u469.7%
 
v469.7%
 
i469.7%
 
N306.3%
 
c306.3%
 
e306.3%
 
m306.3%
 
J163.4%
 
á163.4%
 
o163.4%
 
F163.4%
 
l163.4%
 
r163.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
_92100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII55297.2%
 
None162.8%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_9216.7%
 
a6211.2%
 
n6010.9%
 
u468.3%
 
v468.3%
 
i468.3%
 
N305.4%
 
c305.4%
 
e305.4%
 
m305.4%
 
J162.9%
 
o162.9%
 
F162.9%
 
l162.9%
 
r162.9%
 

Most frequent None characters

ValueCountFrequency (%) 
á16100.0%
 

tool_docker
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size496.0 B
Nunca_nem_vi
21 
Já_ouvi_Falar
16 
Já_trabalhei
ValueCountFrequency (%) 
Nunca_nem_vi2145.7%
 
Já_ouvi_Falar1634.8%
 
Já_trabalhei919.6%
 
2020-12-02T15:19:36.245767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:36.369244image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:36.519214image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length12
Mean length12.34782609
Min length12

Overview of Unicode Properties

Unique unicode characters19
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_8314.6%
 
a7112.5%
 
i468.1%
 
n427.4%
 
u376.5%
 
v376.5%
 
e305.3%
 
J254.4%
 
á254.4%
 
r254.4%
 
l254.4%
 
N213.7%
 
c213.7%
 
m213.7%
 
o162.8%
 
F162.8%
 
t91.6%
 
b91.6%
 
h91.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter42374.5%
 
Connector Punctuation8314.6%
 
Uppercase Letter6210.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J2540.3%
 
N2133.9%
 
F1625.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a7116.8%
 
i4610.9%
 
n429.9%
 
u378.7%
 
v378.7%
 
e307.1%
 
á255.9%
 
r255.9%
 
l255.9%
 
c215.0%
 
m215.0%
 
o163.8%
 
t92.1%
 
b92.1%
 
h92.1%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_83100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin48585.4%
 
Common8314.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a7114.6%
 
i469.5%
 
n428.7%
 
u377.6%
 
v377.6%
 
e306.2%
 
J255.2%
 
á255.2%
 
r255.2%
 
l255.2%
 
N214.3%
 
c214.3%
 
m214.3%
 
o163.3%
 
F163.3%
 
t91.9%
 
b91.9%
 
h91.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
_83100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII54395.6%
 
None254.4%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_8315.3%
 
a7113.1%
 
i468.5%
 
n427.7%
 
u376.8%
 
v376.8%
 
e305.5%
 
J254.6%
 
r254.6%
 
l254.6%
 
N213.9%
 
c213.9%
 
m213.9%
 
o162.9%
 
F162.9%
 
t91.7%
 
b91.7%
 
h91.7%
 

Most frequent None characters

ValueCountFrequency (%) 
á25100.0%
 

tool_ssas
Categorical

Distinct3
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size496.0 B
Nunca_nem_vi
30 
Já_ouvi_Falar
15 
Já_trabalhei
 
1
ValueCountFrequency (%) 
Nunca_nem_vi3065.2%
 
Já_ouvi_Falar1532.6%
 
Já_trabalhei12.2%
 
2020-12-02T15:19:36.681882image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)2.2%
2020-12-02T15:19:36.794495image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:36.902981image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length12
Mean length12.32608696
Min length12

Overview of Unicode Properties

Unique unicode characters19
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_9116.0%
 
a6210.9%
 
n6010.6%
 
i468.1%
 
u457.9%
 
v457.9%
 
e315.5%
 
N305.3%
 
c305.3%
 
m305.3%
 
J162.8%
 
á162.8%
 
l162.8%
 
r162.8%
 
o152.6%
 
F152.6%
 
t10.2%
 
b10.2%
 
h10.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter41573.2%
 
Connector Punctuation9116.0%
 
Uppercase Letter6110.8%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N3049.2%
 
J1626.2%
 
F1524.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a6214.9%
 
n6014.5%
 
i4611.1%
 
u4510.8%
 
v4510.8%
 
e317.5%
 
c307.2%
 
m307.2%
 
á163.9%
 
l163.9%
 
r163.9%
 
o153.6%
 
t10.2%
 
b10.2%
 
h10.2%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_91100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin47684.0%
 
Common9116.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a6213.0%
 
n6012.6%
 
i469.7%
 
u459.5%
 
v459.5%
 
e316.5%
 
N306.3%
 
c306.3%
 
m306.3%
 
J163.4%
 
á163.4%
 
l163.4%
 
r163.4%
 
o153.2%
 
F153.2%
 
t10.2%
 
b10.2%
 
h10.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
_91100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII55197.2%
 
None162.8%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_9116.5%
 
a6211.3%
 
n6010.9%
 
i468.3%
 
u458.2%
 
v458.2%
 
e315.6%
 
N305.4%
 
c305.4%
 
m305.4%
 
J162.9%
 
l162.9%
 
r162.9%
 
o152.7%
 
F152.7%
 
t10.2%
 
b10.2%
 
h10.2%
 

Most frequent None characters

ValueCountFrequency (%) 
á16100.0%
 

tool_tensorflow
Categorical

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Já_ouvi_Falar
17 
Nunca_nem_vi
15 
Já_trabalhei
12 
Tenho_domínio
ValueCountFrequency (%) 
Já_ouvi_Falar1737.0%
 
Nunca_nem_vi1532.6%
 
Já_trabalhei1226.1%
 
Tenho_domínio24.3%
 
2020-12-02T15:19:37.067613image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:37.181990image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:37.284358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length12
Mean length12.41304348
Min length12

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_7813.7%
 
a7312.8%
 
i468.1%
 
n346.0%
 
u325.6%
 
v325.6%
 
J295.1%
 
á295.1%
 
l295.1%
 
r295.1%
 
e295.1%
 
o234.0%
 
F173.0%
 
m173.0%
 
N152.6%
 
c152.6%
 
h142.5%
 
t122.1%
 
b122.1%
 
T20.4%
 
d20.4%
 
í20.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter43075.3%
 
Connector Punctuation7813.7%
 
Uppercase Letter6311.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J2946.0%
 
F1727.0%
 
N1523.8%
 
T23.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a7317.0%
 
i4610.7%
 
n347.9%
 
u327.4%
 
v327.4%
 
á296.7%
 
l296.7%
 
r296.7%
 
e296.7%
 
o235.3%
 
m174.0%
 
c153.5%
 
h143.3%
 
t122.8%
 
b122.8%
 
d20.5%
 
í20.5%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_78100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin49386.3%
 
Common7813.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a7314.8%
 
i469.3%
 
n346.9%
 
u326.5%
 
v326.5%
 
J295.9%
 
á295.9%
 
l295.9%
 
r295.9%
 
e295.9%
 
o234.7%
 
F173.4%
 
m173.4%
 
N153.0%
 
c153.0%
 
h142.8%
 
t122.4%
 
b122.4%
 
T20.4%
 
d20.4%
 
í20.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
_78100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII54094.6%
 
None315.4%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_7814.4%
 
a7313.5%
 
i468.5%
 
n346.3%
 
u325.9%
 
v325.9%
 
J295.4%
 
l295.4%
 
r295.4%
 
e295.4%
 
o234.3%
 
F173.1%
 
m173.1%
 
N152.8%
 
c152.8%
 
h142.6%
 
t122.2%
 
b122.2%
 
T20.4%
 
d20.4%
 

Most frequent None characters

ValueCountFrequency (%) 
á2993.5%
 
í26.5%
 

tool_keras
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Nunca_nem_vi
22 
Já_ouvi_Falar
11 
Já_trabalhei
10 
Tenho_domínio
ValueCountFrequency (%) 
Nunca_nem_vi2247.8%
 
Já_ouvi_Falar1123.9%
 
Já_trabalhei1021.7%
 
Tenho_domínio36.5%
 
2020-12-02T15:19:37.456867image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:37.579391image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:37.695869image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length12
Mean length12.30434783
Min length12

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_7914.0%
 
a6411.3%
 
n508.8%
 
i468.1%
 
e356.2%
 
u335.8%
 
v335.8%
 
m254.4%
 
N223.9%
 
c223.9%
 
J213.7%
 
á213.7%
 
l213.7%
 
r213.7%
 
o203.5%
 
h132.3%
 
F111.9%
 
t101.8%
 
b101.8%
 
T30.5%
 
d30.5%
 
í30.5%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter43076.0%
 
Connector Punctuation7914.0%
 
Uppercase Letter5710.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N2238.6%
 
J2136.8%
 
F1119.3%
 
T35.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a6414.9%
 
n5011.6%
 
i4610.7%
 
e358.1%
 
u337.7%
 
v337.7%
 
m255.8%
 
c225.1%
 
á214.9%
 
l214.9%
 
r214.9%
 
o204.7%
 
h133.0%
 
t102.3%
 
b102.3%
 
d30.7%
 
í30.7%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_79100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin48786.0%
 
Common7914.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a6413.1%
 
n5010.3%
 
i469.4%
 
e357.2%
 
u336.8%
 
v336.8%
 
m255.1%
 
N224.5%
 
c224.5%
 
J214.3%
 
á214.3%
 
l214.3%
 
r214.3%
 
o204.1%
 
h132.7%
 
F112.3%
 
t102.1%
 
b102.1%
 
T30.6%
 
d30.6%
 
í30.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
_79100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII54295.8%
 
None244.2%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_7914.6%
 
a6411.8%
 
n509.2%
 
i468.5%
 
e356.5%
 
u336.1%
 
v336.1%
 
m254.6%
 
N224.1%
 
c224.1%
 
J213.9%
 
l213.9%
 
r213.9%
 
o203.7%
 
h132.4%
 
F112.0%
 
t101.8%
 
b101.8%
 
T30.6%
 
d30.6%
 

Most frequent None characters

ValueCountFrequency (%) 
á2187.5%
 
í312.5%
 

tool_c++
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Já_ouvi_Falar
20 
Já_trabalhei
20 
Tenho_domínio
Nunca_nem_vi
 
2
ValueCountFrequency (%) 
Já_ouvi_Falar2043.5%
 
Já_trabalhei2043.5%
 
Tenho_domínio48.7%
 
Nunca_nem_vi24.3%
 
2020-12-02T15:19:37.857558image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:37.975931image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:38.090608image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length13
Mean length12.52173913
Min length12

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a8214.2%
 
_6811.8%
 
i468.0%
 
J406.9%
 
á406.9%
 
l406.9%
 
r406.9%
 
o325.6%
 
e264.5%
 
h244.2%
 
u223.8%
 
v223.8%
 
F203.5%
 
t203.5%
 
b203.5%
 
n122.1%
 
m61.0%
 
T40.7%
 
d40.7%
 
í40.7%
 
N20.3%
 
c20.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter44276.7%
 
Connector Punctuation6811.8%
 
Uppercase Letter6611.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J4060.6%
 
F2030.3%
 
T46.1%
 
N23.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a8218.6%
 
i4610.4%
 
á409.0%
 
l409.0%
 
r409.0%
 
o327.2%
 
e265.9%
 
h245.4%
 
u225.0%
 
v225.0%
 
t204.5%
 
b204.5%
 
n122.7%
 
m61.4%
 
d40.9%
 
í40.9%
 
c20.5%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_68100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin50888.2%
 
Common6811.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a8216.1%
 
i469.1%
 
J407.9%
 
á407.9%
 
l407.9%
 
r407.9%
 
o326.3%
 
e265.1%
 
h244.7%
 
u224.3%
 
v224.3%
 
F203.9%
 
t203.9%
 
b203.9%
 
n122.4%
 
m61.2%
 
T40.8%
 
d40.8%
 
í40.8%
 
N20.4%
 
c20.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
_68100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII53292.4%
 
None447.6%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a8215.4%
 
_6812.8%
 
i468.6%
 
J407.5%
 
l407.5%
 
r407.5%
 
o326.0%
 
e264.9%
 
h244.5%
 
u224.1%
 
v224.1%
 
F203.8%
 
t203.8%
 
b203.8%
 
n122.3%
 
m61.1%
 
T40.8%
 
d40.8%
 
N20.4%
 
c20.4%
 

Most frequent None characters

ValueCountFrequency (%) 
á4090.9%
 
í49.1%
 

tool_C#
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size496.0 B
Já_ouvi_Falar
34 
Já_trabalhei
Nunca_nem_vi
 
3
ValueCountFrequency (%) 
Já_ouvi_Falar3473.9%
 
Já_trabalhei919.6%
 
Nunca_nem_vi36.5%
 
2020-12-02T15:19:38.260476image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:38.362241image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:38.465539image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length13
Mean length12.73913043
Min length12

Overview of Unicode Properties

Unique unicode characters19
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a8915.2%
 
_8314.2%
 
i467.8%
 
J437.3%
 
á437.3%
 
l437.3%
 
r437.3%
 
u376.3%
 
v376.3%
 
o345.8%
 
F345.8%
 
e122.0%
 
t91.5%
 
b91.5%
 
h91.5%
 
n61.0%
 
N30.5%
 
c30.5%
 
m30.5%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter42372.2%
 
Connector Punctuation8314.2%
 
Uppercase Letter8013.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J4353.8%
 
F3442.5%
 
N33.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a8921.0%
 
i4610.9%
 
á4310.2%
 
l4310.2%
 
r4310.2%
 
u378.7%
 
v378.7%
 
o348.0%
 
e122.8%
 
t92.1%
 
b92.1%
 
h92.1%
 
n61.4%
 
c30.7%
 
m30.7%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_83100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin50385.8%
 
Common8314.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a8917.7%
 
i469.1%
 
J438.5%
 
á438.5%
 
l438.5%
 
r438.5%
 
u377.4%
 
v377.4%
 
o346.8%
 
F346.8%
 
e122.4%
 
t91.8%
 
b91.8%
 
h91.8%
 
n61.2%
 
N30.6%
 
c30.6%
 
m30.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
_83100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII54392.7%
 
None437.3%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a8916.4%
 
_8315.3%
 
i468.5%
 
J437.9%
 
l437.9%
 
r437.9%
 
u376.8%
 
v376.8%
 
o346.3%
 
F346.3%
 
e122.2%
 
t91.7%
 
b91.7%
 
h91.7%
 
n61.1%
 
N30.6%
 
c30.6%
 
m30.6%
 

Most frequent None characters

ValueCountFrequency (%) 
á43100.0%
 

tool_pytorch
Categorical

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Nunca_nem_vi
21 
Já_ouvi_Falar
18 
Já_trabalhei
Tenho_domínio
 
1
ValueCountFrequency (%) 
Nunca_nem_vi2145.7%
 
Já_ouvi_Falar1839.1%
 
Já_trabalhei613.0%
 
Tenho_domínio12.2%
 
2020-12-02T15:19:38.629119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)2.2%
2020-12-02T15:19:38.738930image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:38.850788image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length12
Mean length12.41304348
Min length12

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_8514.9%
 
a6912.1%
 
i468.1%
 
n447.7%
 
u396.8%
 
v396.8%
 
e284.9%
 
J244.2%
 
á244.2%
 
l244.2%
 
r244.2%
 
m223.9%
 
o213.7%
 
N213.7%
 
c213.7%
 
F183.2%
 
h71.2%
 
t61.1%
 
b61.1%
 
T10.2%
 
d10.2%
 
í10.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter42273.9%
 
Connector Punctuation8514.9%
 
Uppercase Letter6411.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J2437.5%
 
N2132.8%
 
F1828.1%
 
T11.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a6916.4%
 
i4610.9%
 
n4410.4%
 
u399.2%
 
v399.2%
 
e286.6%
 
á245.7%
 
l245.7%
 
r245.7%
 
m225.2%
 
o215.0%
 
c215.0%
 
h71.7%
 
t61.4%
 
b61.4%
 
d10.2%
 
í10.2%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_85100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin48685.1%
 
Common8514.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a6914.2%
 
i469.5%
 
n449.1%
 
u398.0%
 
v398.0%
 
e285.8%
 
J244.9%
 
á244.9%
 
l244.9%
 
r244.9%
 
m224.5%
 
o214.3%
 
N214.3%
 
c214.3%
 
F183.7%
 
h71.4%
 
t61.2%
 
b61.2%
 
T10.2%
 
d10.2%
 
í10.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
_85100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII54695.6%
 
None254.4%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_8515.6%
 
a6912.6%
 
i468.4%
 
n448.1%
 
u397.1%
 
v397.1%
 
e285.1%
 
J244.4%
 
l244.4%
 
r244.4%
 
m224.0%
 
o213.8%
 
N213.8%
 
c213.8%
 
F183.3%
 
h71.3%
 
t61.1%
 
b61.1%
 
T10.2%
 
d10.2%
 

Most frequent None characters

ValueCountFrequency (%) 
á2496.0%
 
í14.0%
 

tool_sklearn
Categorical

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Nunca_nem_vi
15 
Já_trabalhei
15 
Já_ouvi_Falar
12 
Tenho_domínio
ValueCountFrequency (%) 
Nunca_nem_vi1532.6%
 
Já_trabalhei1532.6%
 
Já_ouvi_Falar1226.1%
 
Tenho_domínio48.7%
 
2020-12-02T15:19:38.997869image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:39.096570image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:39.209038image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length12
Mean length12.34782609
Min length12

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_7312.9%
 
a6912.1%
 
i468.1%
 
n386.7%
 
e346.0%
 
u274.8%
 
v274.8%
 
J274.8%
 
á274.8%
 
l274.8%
 
r274.8%
 
o244.2%
 
h193.3%
 
m193.3%
 
N152.6%
 
c152.6%
 
t152.6%
 
b152.6%
 
F122.1%
 
T40.7%
 
d40.7%
 
í40.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter43776.9%
 
Connector Punctuation7312.9%
 
Uppercase Letter5810.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J2746.6%
 
N1525.9%
 
F1220.7%
 
T46.9%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a6915.8%
 
i4610.5%
 
n388.7%
 
e347.8%
 
u276.2%
 
v276.2%
 
á276.2%
 
l276.2%
 
r276.2%
 
o245.5%
 
h194.3%
 
m194.3%
 
c153.4%
 
t153.4%
 
b153.4%
 
d40.9%
 
í40.9%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_73100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin49587.1%
 
Common7312.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a6913.9%
 
i469.3%
 
n387.7%
 
e346.9%
 
u275.5%
 
v275.5%
 
J275.5%
 
á275.5%
 
l275.5%
 
r275.5%
 
o244.8%
 
h193.8%
 
m193.8%
 
N153.0%
 
c153.0%
 
t153.0%
 
b153.0%
 
F122.4%
 
T40.8%
 
d40.8%
 
í40.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
_73100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII53794.5%
 
None315.5%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_7313.6%
 
a6912.8%
 
i468.6%
 
n387.1%
 
e346.3%
 
u275.0%
 
v275.0%
 
J275.0%
 
l275.0%
 
r275.0%
 
o244.5%
 
h193.5%
 
m193.5%
 
N152.8%
 
c152.8%
 
t152.8%
 
b152.8%
 
F122.2%
 
T40.7%
 
d40.7%
 

Most frequent None characters

ValueCountFrequency (%) 
á2787.1%
 
í412.9%
 

tool_git
Categorical

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Já_ouvi_Falar
19 
Já_trabalhei
16 
Tenho_domínio
Nunca_nem_vi
ValueCountFrequency (%) 
Já_ouvi_Falar1941.3%
 
Já_trabalhei1634.8%
 
Tenho_domínio613.0%
 
Nunca_nem_vi510.9%
 
2020-12-02T15:19:39.361780image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:39.455261image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:39.574038image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length13
Mean length12.54347826
Min length12

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a7513.0%
 
_7012.1%
 
i468.0%
 
o376.4%
 
J356.1%
 
á356.1%
 
l356.1%
 
r356.1%
 
e274.7%
 
u244.2%
 
v244.2%
 
n223.8%
 
h223.8%
 
F193.3%
 
t162.8%
 
b162.8%
 
m111.9%
 
T61.0%
 
d61.0%
 
í61.0%
 
N50.9%
 
c50.9%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter44276.6%
 
Connector Punctuation7012.1%
 
Uppercase Letter6511.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J3553.8%
 
F1929.2%
 
T69.2%
 
N57.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a7517.0%
 
i4610.4%
 
o378.4%
 
á357.9%
 
l357.9%
 
r357.9%
 
e276.1%
 
u245.4%
 
v245.4%
 
n225.0%
 
h225.0%
 
t163.6%
 
b163.6%
 
m112.5%
 
d61.4%
 
í61.4%
 
c51.1%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_70100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin50787.9%
 
Common7012.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a7514.8%
 
i469.1%
 
o377.3%
 
J356.9%
 
á356.9%
 
l356.9%
 
r356.9%
 
e275.3%
 
u244.7%
 
v244.7%
 
n224.3%
 
h224.3%
 
F193.7%
 
t163.2%
 
b163.2%
 
m112.2%
 
T61.2%
 
d61.2%
 
í61.2%
 
N51.0%
 
c51.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
_70100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII53692.9%
 
None417.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a7514.0%
 
_7013.1%
 
i468.6%
 
o376.9%
 
J356.5%
 
l356.5%
 
r356.5%
 
e275.0%
 
u244.5%
 
v244.5%
 
n224.1%
 
h224.1%
 
F193.5%
 
t163.0%
 
b163.0%
 
m112.1%
 
T61.1%
 
d61.1%
 
N50.9%
 
c50.9%
 

Most frequent None characters

ValueCountFrequency (%) 
á3585.4%
 
í614.6%
 

tool_pbi
Categorical

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Já_ouvi_Falar
19 
Já_trabalhei
17 
Tenho_domínio
Nunca_nem_vi
ValueCountFrequency (%) 
Já_ouvi_Falar1941.3%
 
Já_trabalhei1737.0%
 
Tenho_domínio613.0%
 
Nunca_nem_vi48.7%
 
2020-12-02T15:19:39.710407image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:39.817434image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:39.921181image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length13
Mean length12.54347826
Min length12

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a7613.2%
 
_6912.0%
 
i468.0%
 
o376.4%
 
J366.2%
 
á366.2%
 
l366.2%
 
r366.2%
 
e274.7%
 
u234.0%
 
v234.0%
 
h234.0%
 
n203.5%
 
F193.3%
 
t172.9%
 
b172.9%
 
m101.7%
 
T61.0%
 
d61.0%
 
í61.0%
 
N40.7%
 
c40.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter44376.8%
 
Connector Punctuation6912.0%
 
Uppercase Letter6511.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J3655.4%
 
F1929.2%
 
T69.2%
 
N46.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a7617.2%
 
i4610.4%
 
o378.4%
 
á368.1%
 
l368.1%
 
r368.1%
 
e276.1%
 
u235.2%
 
v235.2%
 
h235.2%
 
n204.5%
 
t173.8%
 
b173.8%
 
m102.3%
 
d61.4%
 
í61.4%
 
c40.9%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_69100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin50888.0%
 
Common6912.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a7615.0%
 
i469.1%
 
o377.3%
 
J367.1%
 
á367.1%
 
l367.1%
 
r367.1%
 
e275.3%
 
u234.5%
 
v234.5%
 
h234.5%
 
n203.9%
 
F193.7%
 
t173.3%
 
b173.3%
 
m102.0%
 
T61.2%
 
d61.2%
 
í61.2%
 
N40.8%
 
c40.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
_69100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII53592.7%
 
None427.3%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a7614.2%
 
_6912.9%
 
i468.6%
 
o376.9%
 
J366.7%
 
l366.7%
 
r366.7%
 
e275.0%
 
u234.3%
 
v234.3%
 
h234.3%
 
n203.7%
 
F193.6%
 
t173.2%
 
b173.2%
 
m101.9%
 
T61.1%
 
d61.1%
 
N40.7%
 
c40.7%
 

Most frequent None characters

ValueCountFrequency (%) 
á3685.7%
 
í614.3%
 

tool_tableau
Categorical

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Já_ouvi_Falar
23 
Nunca_nem_vi
17 
Já_trabalhei
Tenho_domínio
 
1
ValueCountFrequency (%) 
Já_ouvi_Falar2350.0%
 
Nunca_nem_vi1737.0%
 
Já_trabalhei510.9%
 
Tenho_domínio12.2%
 
2020-12-02T15:19:40.057988image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)2.2%
2020-12-02T15:19:40.153823image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:40.259871image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length13
Mean length12.52173913
Min length12

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_8614.9%
 
a7312.7%
 
i468.0%
 
u406.9%
 
v406.9%
 
n366.2%
 
J284.9%
 
á284.9%
 
l284.9%
 
r284.9%
 
o264.5%
 
F234.0%
 
e234.0%
 
m183.1%
 
N173.0%
 
c173.0%
 
h61.0%
 
t50.9%
 
b50.9%
 
T10.2%
 
d10.2%
 
í10.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter42173.1%
 
Connector Punctuation8614.9%
 
Uppercase Letter6912.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J2840.6%
 
F2333.3%
 
N1724.6%
 
T11.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a7317.3%
 
i4610.9%
 
u409.5%
 
v409.5%
 
n368.6%
 
á286.7%
 
l286.7%
 
r286.7%
 
o266.2%
 
e235.5%
 
m184.3%
 
c174.0%
 
h61.4%
 
t51.2%
 
b51.2%
 
d10.2%
 
í10.2%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_86100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin49085.1%
 
Common8614.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a7314.9%
 
i469.4%
 
u408.2%
 
v408.2%
 
n367.3%
 
J285.7%
 
á285.7%
 
l285.7%
 
r285.7%
 
o265.3%
 
F234.7%
 
e234.7%
 
m183.7%
 
N173.5%
 
c173.5%
 
h61.2%
 
t51.0%
 
b51.0%
 
T10.2%
 
d10.2%
 
í10.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
_86100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII54795.0%
 
None295.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_8615.7%
 
a7313.3%
 
i468.4%
 
u407.3%
 
v407.3%
 
n366.6%
 
J285.1%
 
l285.1%
 
r285.1%
 
o264.8%
 
F234.2%
 
e234.2%
 
m183.3%
 
N173.1%
 
c173.1%
 
h61.1%
 
t50.9%
 
b50.9%
 
T10.2%
 
d10.2%
 

Most frequent None characters

ValueCountFrequency (%) 
á2896.6%
 
í13.4%
 

tool_qlick
Categorical

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size496.0 B
Nunca_nem_vi
28 
Já_ouvi_Falar
18 
ValueCountFrequency (%) 
Nunca_nem_vi2860.9%
 
Já_ouvi_Falar1839.1%
 
2020-12-02T15:19:40.405136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:40.480669image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:40.562413image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length12
Mean length12.39130435
Min length12

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_9216.1%
 
a6411.2%
 
n569.8%
 
u468.1%
 
v468.1%
 
i468.1%
 
N284.9%
 
c284.9%
 
e284.9%
 
m284.9%
 
J183.2%
 
á183.2%
 
o183.2%
 
F183.2%
 
l183.2%
 
r183.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter41472.6%
 
Connector Punctuation9216.1%
 
Uppercase Letter6411.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N2843.8%
 
J1828.1%
 
F1828.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a6415.5%
 
n5613.5%
 
u4611.1%
 
v4611.1%
 
i4611.1%
 
c286.8%
 
e286.8%
 
m286.8%
 
á184.3%
 
o184.3%
 
l184.3%
 
r184.3%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_92100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin47883.9%
 
Common9216.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a6413.4%
 
n5611.7%
 
u469.6%
 
v469.6%
 
i469.6%
 
N285.9%
 
c285.9%
 
e285.9%
 
m285.9%
 
J183.8%
 
á183.8%
 
o183.8%
 
F183.8%
 
l183.8%
 
r183.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
_92100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII55296.8%
 
None183.2%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_9216.7%
 
a6411.6%
 
n5610.1%
 
u468.3%
 
v468.3%
 
i468.3%
 
N285.1%
 
c285.1%
 
e285.1%
 
m285.1%
 
J183.3%
 
o183.3%
 
F183.3%
 
l183.3%
 
r183.3%
 

Most frequent None characters

ValueCountFrequency (%) 
á18100.0%
 

tool_plotly
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Nunca_nem_vi
23 
Já_ouvi_Falar
12 
Já_trabalhei
Tenho_domínio
 
2
ValueCountFrequency (%) 
Nunca_nem_vi2350.0%
 
Já_ouvi_Falar1226.1%
 
Já_trabalhei919.6%
 
Tenho_domínio24.3%
 
2020-12-02T15:19:40.694301image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:40.779885image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:40.891433image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length12
Mean length12.30434783
Min length12

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_8114.3%
 
a6511.5%
 
n508.8%
 
i468.1%
 
u356.2%
 
v356.2%
 
e346.0%
 
m254.4%
 
N234.1%
 
c234.1%
 
J213.7%
 
á213.7%
 
r213.7%
 
l213.7%
 
o183.2%
 
F122.1%
 
h111.9%
 
t91.6%
 
b91.6%
 
T20.4%
 
d20.4%
 
í20.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter42775.4%
 
Connector Punctuation8114.3%
 
Uppercase Letter5810.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N2339.7%
 
J2136.2%
 
F1220.7%
 
T23.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a6515.2%
 
n5011.7%
 
i4610.8%
 
u358.2%
 
v358.2%
 
e348.0%
 
m255.9%
 
c235.4%
 
á214.9%
 
r214.9%
 
l214.9%
 
o184.2%
 
h112.6%
 
t92.1%
 
b92.1%
 
d20.5%
 
í20.5%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_81100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin48585.7%
 
Common8114.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a6513.4%
 
n5010.3%
 
i469.5%
 
u357.2%
 
v357.2%
 
e347.0%
 
m255.2%
 
N234.7%
 
c234.7%
 
J214.3%
 
á214.3%
 
r214.3%
 
l214.3%
 
o183.7%
 
F122.5%
 
h112.3%
 
t91.9%
 
b91.9%
 
T20.4%
 
d20.4%
 
í20.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
_81100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII54395.9%
 
None234.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_8114.9%
 
a6512.0%
 
n509.2%
 
i468.5%
 
u356.4%
 
v356.4%
 
e346.3%
 
m254.6%
 
N234.2%
 
c234.2%
 
J213.9%
 
r213.9%
 
l213.9%
 
o183.3%
 
F122.2%
 
h112.0%
 
t91.7%
 
b91.7%
 
T20.4%
 
d20.4%
 

Most frequent None characters

ValueCountFrequency (%) 
á2191.3%
 
í28.7%
 
Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Só_estudei_nunca_apliquei
17 
Apliquei_em_algum_trabalho
16 
Nunca_estudei_e_nunca_apliquei
Desenvolvi_grandes_projetos_com_isso
ValueCountFrequency (%) 
Só_estudei_nunca_apliquei1737.0%
 
Apliquei_em_algum_trabalho1634.8%
 
Nunca_estudei_e_nunca_apliquei817.4%
 
Desenvolvi_grandes_projetos_com_isso510.9%
 
2020-12-02T15:19:41.042376image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:41.133516image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:41.246441image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length26
Mean length27.41304348
Min length25

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_15112.0%
 
e13510.7%
 
i1179.3%
 
u1159.1%
 
a1118.8%
 
l786.2%
 
n685.4%
 
s504.0%
 
p463.6%
 
t463.6%
 
o413.3%
 
q413.3%
 
c383.0%
 
m372.9%
 
d302.4%
 
r262.1%
 
g211.7%
 
S171.3%
 
ó171.3%
 
A161.3%
 
b161.3%
 
h161.3%
 
v100.8%
 
N80.6%
 
D50.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter106484.4%
 
Connector Punctuation15112.0%
 
Uppercase Letter463.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S1737.0%
 
A1634.8%
 
N817.4%
 
D510.9%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e13512.7%
 
i11711.0%
 
u11510.8%
 
a11110.4%
 
l787.3%
 
n686.4%
 
s504.7%
 
p464.3%
 
t464.3%
 
o413.9%
 
q413.9%
 
c383.6%
 
m373.5%
 
d302.8%
 
r262.4%
 
g212.0%
 
ó171.6%
 
b161.5%
 
h161.5%
 
v100.9%
 
j50.5%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_151100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin111088.0%
 
Common15112.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e13512.2%
 
i11710.5%
 
u11510.4%
 
a11110.0%
 
l787.0%
 
n686.1%
 
s504.5%
 
p464.1%
 
t464.1%
 
o413.7%
 
q413.7%
 
c383.4%
 
m373.3%
 
d302.7%
 
r262.3%
 
g211.9%
 
S171.5%
 
ó171.5%
 
A161.4%
 
b161.4%
 
h161.4%
 
v100.9%
 
N80.7%
 
D50.5%
 
j50.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
_151100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII124498.7%
 
None171.3%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_15112.1%
 
e13510.9%
 
i1179.4%
 
u1159.2%
 
a1118.9%
 
l786.3%
 
n685.5%
 
s504.0%
 
p463.7%
 
t463.7%
 
o413.3%
 
q413.3%
 
c383.1%
 
m373.0%
 
d302.4%
 
r262.1%
 
g211.7%
 
S171.4%
 
A161.3%
 
b161.3%
 
h161.3%
 
v100.8%
 
N80.6%
 
D50.4%
 
j50.4%
 

Most frequent None characters

ValueCountFrequency (%) 
ó17100.0%
 

algoritmo_aprend_nao_super
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Só_estudei_nunca_apliquei
22 
Nunca_estudei_e_nunca_apliquei
12 
Apliquei_em_algum_trabalho
10 
Desenvolvi_grandes_projetos_com_isso
 
2
ValueCountFrequency (%) 
Só_estudei_nunca_apliquei2247.8%
 
Nunca_estudei_e_nunca_apliquei1226.1%
 
Apliquei_em_algum_trabalho1021.7%
 
Desenvolvi_grandes_projetos_com_isso24.3%
 
2020-12-02T15:19:41.385891image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:41.481430image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:41.601548image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length26
Mean length27
Min length25

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_15212.2%
 
e14211.4%
 
u13410.8%
 
i12610.1%
 
a1129.0%
 
n846.8%
 
l665.3%
 
c483.9%
 
t463.7%
 
p463.7%
 
s443.5%
 
q443.5%
 
d362.9%
 
S221.8%
 
ó221.8%
 
m221.8%
 
o201.6%
 
r141.1%
 
N121.0%
 
g121.0%
 
A100.8%
 
b100.8%
 
h100.8%
 
v40.3%
 
D20.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter104484.1%
 
Connector Punctuation15212.2%
 
Uppercase Letter463.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S2247.8%
 
N1226.1%
 
A1021.7%
 
D24.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e14213.6%
 
u13412.8%
 
i12612.1%
 
a11210.7%
 
n848.0%
 
l666.3%
 
c484.6%
 
t464.4%
 
p464.4%
 
s444.2%
 
q444.2%
 
d363.4%
 
ó222.1%
 
m222.1%
 
o201.9%
 
r141.3%
 
g121.1%
 
b101.0%
 
h101.0%
 
v40.4%
 
j20.2%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_152100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin109087.8%
 
Common15212.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e14213.0%
 
u13412.3%
 
i12611.6%
 
a11210.3%
 
n847.7%
 
l666.1%
 
c484.4%
 
t464.2%
 
p464.2%
 
s444.0%
 
q444.0%
 
d363.3%
 
S222.0%
 
ó222.0%
 
m222.0%
 
o201.8%
 
r141.3%
 
N121.1%
 
g121.1%
 
A100.9%
 
b100.9%
 
h100.9%
 
v40.4%
 
D20.2%
 
j20.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
_152100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII122098.2%
 
None221.8%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_15212.5%
 
e14211.6%
 
u13411.0%
 
i12610.3%
 
a1129.2%
 
n846.9%
 
l665.4%
 
c483.9%
 
t463.8%
 
p463.8%
 
s443.6%
 
q443.6%
 
d363.0%
 
S221.8%
 
m221.8%
 
o201.6%
 
r141.1%
 
N121.0%
 
g121.0%
 
A100.8%
 
b100.8%
 
h100.8%
 
v40.3%
 
D20.2%
 
j20.2%
 

Most frequent None characters

ValueCountFrequency (%) 
ó22100.0%
 
Distinct3
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size496.0 B
Só_estudei_nunca_apliquei
23 
Nunca_estudei_e_nunca_apliquei
21 
Apliquei_em_algum_trabalho
 
2
ValueCountFrequency (%) 
Só_estudei_nunca_apliquei2350.0%
 
Nunca_estudei_e_nunca_apliquei2145.7%
 
Apliquei_em_algum_trabalho24.3%
 
2020-12-02T15:19:41.745019image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:41.830287image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:41.934855image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length25.5
Mean length27.32608696
Min length25

Overview of Unicode Properties

Unique unicode characters23
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_15912.6%
 
u15712.5%
 
e15712.5%
 
i13610.8%
 
a1159.1%
 
n1098.7%
 
c655.2%
 
l504.0%
 
t463.7%
 
p463.7%
 
q463.7%
 
s443.5%
 
d443.5%
 
S231.8%
 
ó231.8%
 
N211.7%
 
m40.3%
 
A20.2%
 
g20.2%
 
r20.2%
 
b20.2%
 
h20.2%
 
o20.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter105283.7%
 
Connector Punctuation15912.6%
 
Uppercase Letter463.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S2350.0%
 
N2145.7%
 
A24.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
u15714.9%
 
e15714.9%
 
i13612.9%
 
a11510.9%
 
n10910.4%
 
c656.2%
 
l504.8%
 
t464.4%
 
p464.4%
 
q464.4%
 
s444.2%
 
d444.2%
 
ó232.2%
 
m40.4%
 
g20.2%
 
r20.2%
 
b20.2%
 
h20.2%
 
o20.2%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_159100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin109887.4%
 
Common15912.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
u15714.3%
 
e15714.3%
 
i13612.4%
 
a11510.5%
 
n1099.9%
 
c655.9%
 
l504.6%
 
t464.2%
 
p464.2%
 
q464.2%
 
s444.0%
 
d444.0%
 
S232.1%
 
ó232.1%
 
N211.9%
 
m40.4%
 
A20.2%
 
g20.2%
 
r20.2%
 
b20.2%
 
h20.2%
 
o20.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
_159100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII123498.2%
 
None231.8%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_15912.9%
 
u15712.7%
 
e15712.7%
 
i13611.0%
 
a1159.3%
 
n1098.8%
 
c655.3%
 
l504.1%
 
t463.7%
 
p463.7%
 
q463.7%
 
s443.6%
 
d443.6%
 
S231.9%
 
N211.7%
 
m40.3%
 
A20.2%
 
g20.2%
 
r20.2%
 
b20.2%
 
h20.2%
 
o20.2%
 

Most frequent None characters

ValueCountFrequency (%) 
ó23100.0%
 
Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Nunca_estudei_e_nunca_apliquei
20 
Só_estudei_nunca_apliquei
15 
Apliquei_em_algum_trabalho
Desenvolvi_grandes_projetos_com_isso
ValueCountFrequency (%) 
Nunca_estudei_e_nunca_apliquei2043.5%
 
Só_estudei_nunca_apliquei1532.6%
 
Apliquei_em_algum_trabalho715.2%
 
Desenvolvi_grandes_projetos_com_isso48.7%
 
2020-12-02T15:19:42.080189image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:42.190806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:42.673698image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length30
Mean length28.2826087
Min length25

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_16212.5%
 
e15511.9%
 
u13910.7%
 
i1279.8%
 
a1158.8%
 
n987.5%
 
l604.6%
 
c594.5%
 
s554.2%
 
p463.5%
 
t463.5%
 
q423.2%
 
d393.0%
 
o272.1%
 
N201.5%
 
m181.4%
 
r151.2%
 
S151.2%
 
ó151.2%
 
g110.8%
 
v80.6%
 
A70.5%
 
b70.5%
 
h70.5%
 
D40.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter109384.0%
 
Connector Punctuation16212.5%
 
Uppercase Letter463.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N2043.5%
 
S1532.6%
 
A715.2%
 
D48.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e15514.2%
 
u13912.7%
 
i12711.6%
 
a11510.5%
 
n989.0%
 
l605.5%
 
c595.4%
 
s555.0%
 
p464.2%
 
t464.2%
 
q423.8%
 
d393.6%
 
o272.5%
 
m181.6%
 
r151.4%
 
ó151.4%
 
g111.0%
 
v80.7%
 
b70.6%
 
h70.6%
 
j40.4%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_162100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin113987.5%
 
Common16212.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e15513.6%
 
u13912.2%
 
i12711.2%
 
a11510.1%
 
n988.6%
 
l605.3%
 
c595.2%
 
s554.8%
 
p464.0%
 
t464.0%
 
q423.7%
 
d393.4%
 
o272.4%
 
N201.8%
 
m181.6%
 
r151.3%
 
S151.3%
 
ó151.3%
 
g111.0%
 
v80.7%
 
A70.6%
 
b70.6%
 
h70.6%
 
D40.4%
 
j40.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
_162100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII128698.8%
 
None151.2%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_16212.6%
 
e15512.1%
 
u13910.8%
 
i1279.9%
 
a1158.9%
 
n987.6%
 
l604.7%
 
c594.6%
 
s554.3%
 
p463.6%
 
t463.6%
 
q423.3%
 
d393.0%
 
o272.1%
 
N201.6%
 
m181.4%
 
r151.2%
 
S151.2%
 
g110.9%
 
v80.6%
 
A70.5%
 
b70.5%
 
h70.5%
 
D40.3%
 
j40.3%
 

Most frequent None characters

ValueCountFrequency (%) 
ó15100.0%
 
Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Nunca_estudei_e_nunca_apliquei
17 
Apliquei_em_algum_trabalho
14 
Só_estudei_nunca_apliquei
12 
Desenvolvi_grandes_projetos_com_isso
ValueCountFrequency (%) 
Nunca_estudei_e_nunca_apliquei1737.0%
 
Apliquei_em_algum_trabalho1430.4%
 
Só_estudei_nunca_apliquei1226.1%
 
Desenvolvi_grandes_projetos_com_isso36.5%
 
2020-12-02T15:19:42.804382image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:42.903850image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:43.021979image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length26
Mean length27.86956522
Min length25

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_15812.3%
 
e14411.2%
 
u13210.3%
 
i1219.4%
 
a1209.4%
 
n816.3%
 
l745.8%
 
c493.8%
 
p463.6%
 
t463.6%
 
s443.4%
 
q433.4%
 
d322.5%
 
m312.4%
 
o292.3%
 
r201.6%
 
g171.3%
 
N171.3%
 
A141.1%
 
b141.1%
 
h141.1%
 
S120.9%
 
ó120.9%
 
v60.5%
 
D30.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter107884.1%
 
Connector Punctuation15812.3%
 
Uppercase Letter463.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N1737.0%
 
A1430.4%
 
S1226.1%
 
D36.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e14413.4%
 
u13212.2%
 
i12111.2%
 
a12011.1%
 
n817.5%
 
l746.9%
 
c494.5%
 
p464.3%
 
t464.3%
 
s444.1%
 
q434.0%
 
d323.0%
 
m312.9%
 
o292.7%
 
r201.9%
 
g171.6%
 
b141.3%
 
h141.3%
 
ó121.1%
 
v60.6%
 
j30.3%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_158100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin112487.7%
 
Common15812.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e14412.8%
 
u13211.7%
 
i12110.8%
 
a12010.7%
 
n817.2%
 
l746.6%
 
c494.4%
 
p464.1%
 
t464.1%
 
s443.9%
 
q433.8%
 
d322.8%
 
m312.8%
 
o292.6%
 
r201.8%
 
g171.5%
 
N171.5%
 
A141.2%
 
b141.2%
 
h141.2%
 
S121.1%
 
ó121.1%
 
v60.5%
 
D30.3%
 
j30.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
_158100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII127099.1%
 
None120.9%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_15812.4%
 
e14411.3%
 
u13210.4%
 
i1219.5%
 
a1209.4%
 
n816.4%
 
l745.8%
 
c493.9%
 
p463.6%
 
t463.6%
 
s443.5%
 
q433.4%
 
d322.5%
 
m312.4%
 
o292.3%
 
r201.6%
 
g171.3%
 
N171.3%
 
A141.1%
 
b141.1%
 
h141.1%
 
S120.9%
 
v60.5%
 
D30.2%
 
j30.2%
 

Most frequent None characters

ValueCountFrequency (%) 
ó12100.0%
 
Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Nunca_estudei_e_nunca_apliquei
24 
Só_estudei_nunca_apliquei
15 
Apliquei_em_algum_trabalho
Desenvolvi_grandes_projetos_com_isso
 
2
ValueCountFrequency (%) 
Nunca_estudei_e_nunca_apliquei2452.2%
 
Só_estudei_nunca_apliquei1532.6%
 
Apliquei_em_algum_trabalho510.9%
 
Desenvolvi_grandes_projetos_com_isso24.3%
 
2020-12-02T15:19:43.163233image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:43.250182image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:43.359633image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length30
Mean length28.19565217
Min length25

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_16412.6%
 
e15912.3%
 
u15111.6%
 
i13110.1%
 
a1199.2%
 
n1068.2%
 
c655.0%
 
l564.3%
 
s493.8%
 
p463.5%
 
t463.5%
 
q443.4%
 
d413.2%
 
N241.9%
 
o151.2%
 
S151.2%
 
ó151.2%
 
m120.9%
 
r90.7%
 
g70.5%
 
A50.4%
 
b50.4%
 
h50.4%
 
v40.3%
 
D20.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter108783.8%
 
Connector Punctuation16412.6%
 
Uppercase Letter463.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N2452.2%
 
S1532.6%
 
A510.9%
 
D24.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e15914.6%
 
u15113.9%
 
i13112.1%
 
a11910.9%
 
n1069.8%
 
c656.0%
 
l565.2%
 
s494.5%
 
p464.2%
 
t464.2%
 
q444.0%
 
d413.8%
 
o151.4%
 
ó151.4%
 
m121.1%
 
r90.8%
 
g70.6%
 
b50.5%
 
h50.5%
 
v40.4%
 
j20.2%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_164100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin113387.4%
 
Common16412.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e15914.0%
 
u15113.3%
 
i13111.6%
 
a11910.5%
 
n1069.4%
 
c655.7%
 
l564.9%
 
s494.3%
 
p464.1%
 
t464.1%
 
q443.9%
 
d413.6%
 
N242.1%
 
o151.3%
 
S151.3%
 
ó151.3%
 
m121.1%
 
r90.8%
 
g70.6%
 
A50.4%
 
b50.4%
 
h50.4%
 
v40.4%
 
D20.2%
 
j20.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
_164100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII128298.8%
 
None151.2%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_16412.8%
 
e15912.4%
 
u15111.8%
 
i13110.2%
 
a1199.3%
 
n1068.3%
 
c655.1%
 
l564.4%
 
s493.8%
 
p463.6%
 
t463.6%
 
q443.4%
 
d413.2%
 
N241.9%
 
o151.2%
 
S151.2%
 
m120.9%
 
r90.7%
 
g70.5%
 
A50.4%
 
b50.4%
 
h50.4%
 
v40.3%
 
D20.2%
 
j20.2%
 

Most frequent None characters

ValueCountFrequency (%) 
ó15100.0%
 

algoritmo_visao_computacional
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Nunca_estudei_e_nunca_apliquei
27 
Só_estudei_nunca_apliquei
Apliquei_em_algum_trabalho
Desenvolvi_grandes_projetos_com_isso
 
2
ValueCountFrequency (%) 
Nunca_estudei_e_nunca_apliquei2758.7%
 
Só_estudei_nunca_apliquei919.6%
 
Apliquei_em_algum_trabalho817.4%
 
Desenvolvi_grandes_projetos_com_isso24.3%
 
2020-12-02T15:19:43.538436image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:43.628977image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:43.744145image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length30
Mean length28.58695652
Min length25

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_16712.7%
 
e15912.1%
 
u15111.5%
 
i1289.7%
 
a1259.5%
 
n1037.8%
 
c654.9%
 
l624.7%
 
p463.5%
 
t463.5%
 
s463.5%
 
q443.3%
 
d382.9%
 
N272.1%
 
m181.4%
 
o181.4%
 
r120.9%
 
g100.8%
 
S90.7%
 
ó90.7%
 
A80.6%
 
b80.6%
 
h80.6%
 
v40.3%
 
D20.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter110283.8%
 
Connector Punctuation16712.7%
 
Uppercase Letter463.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N2758.7%
 
S919.6%
 
A817.4%
 
D24.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e15914.4%
 
u15113.7%
 
i12811.6%
 
a12511.3%
 
n1039.3%
 
c655.9%
 
l625.6%
 
p464.2%
 
t464.2%
 
s464.2%
 
q444.0%
 
d383.4%
 
m181.6%
 
o181.6%
 
r121.1%
 
g100.9%
 
ó90.8%
 
b80.7%
 
h80.7%
 
v40.4%
 
j20.2%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_167100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin114887.3%
 
Common16712.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e15913.9%
 
u15113.2%
 
i12811.1%
 
a12510.9%
 
n1039.0%
 
c655.7%
 
l625.4%
 
p464.0%
 
t464.0%
 
s464.0%
 
q443.8%
 
d383.3%
 
N272.4%
 
m181.6%
 
o181.6%
 
r121.0%
 
g100.9%
 
S90.8%
 
ó90.8%
 
A80.7%
 
b80.7%
 
h80.7%
 
v40.3%
 
D20.2%
 
j20.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
_167100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII130699.3%
 
None90.7%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_16712.8%
 
e15912.2%
 
u15111.6%
 
i1289.8%
 
a1259.6%
 
n1037.9%
 
c655.0%
 
l624.7%
 
p463.5%
 
t463.5%
 
s463.5%
 
q443.4%
 
d382.9%
 
N272.1%
 
m181.4%
 
o181.4%
 
r120.9%
 
g100.8%
 
S90.7%
 
A80.6%
 
b80.6%
 
h80.6%
 
v40.3%
 
D20.2%
 
j20.2%
 

Most frequent None characters

ValueCountFrequency (%) 
ó9100.0%
 

algoritmo_pdi
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Nunca_estudei_e_nunca_apliquei
17 
Só_estudei_nunca_apliquei
15 
Apliquei_em_algum_trabalho
12 
Desenvolvi_grandes_projetos_com_isso
ValueCountFrequency (%) 
Nunca_estudei_e_nunca_apliquei1737.0%
 
Só_estudei_nunca_apliquei1532.6%
 
Apliquei_em_algum_trabalho1226.1%
 
Desenvolvi_grandes_projetos_com_isso24.3%
 
2020-12-02T15:19:43.898773image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:44.008632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:44.142647image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length26
Mean length27.58695652
Min length25

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_15712.4%
 
e14511.4%
 
u13710.8%
 
i1249.8%
 
a1199.4%
 
n856.7%
 
l705.5%
 
c514.0%
 
t463.6%
 
p463.6%
 
q443.5%
 
s423.3%
 
d342.7%
 
m262.0%
 
o221.7%
 
N171.3%
 
r161.3%
 
S151.2%
 
ó151.2%
 
g141.1%
 
A120.9%
 
b120.9%
 
h120.9%
 
v40.3%
 
D20.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter106684.0%
 
Connector Punctuation15712.4%
 
Uppercase Letter463.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N1737.0%
 
S1532.6%
 
A1226.1%
 
D24.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e14513.6%
 
u13712.9%
 
i12411.6%
 
a11911.2%
 
n858.0%
 
l706.6%
 
c514.8%
 
t464.3%
 
p464.3%
 
q444.1%
 
s423.9%
 
d343.2%
 
m262.4%
 
o222.1%
 
r161.5%
 
ó151.4%
 
g141.3%
 
b121.1%
 
h121.1%
 
v40.4%
 
j20.2%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_157100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin111287.6%
 
Common15712.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e14513.0%
 
u13712.3%
 
i12411.2%
 
a11910.7%
 
n857.6%
 
l706.3%
 
c514.6%
 
t464.1%
 
p464.1%
 
q444.0%
 
s423.8%
 
d343.1%
 
m262.3%
 
o222.0%
 
N171.5%
 
r161.4%
 
S151.3%
 
ó151.3%
 
g141.3%
 
A121.1%
 
b121.1%
 
h121.1%
 
v40.4%
 
D20.2%
 
j20.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
_157100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII125498.8%
 
None151.2%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_15712.5%
 
e14511.6%
 
u13710.9%
 
i1249.9%
 
a1199.5%
 
n856.8%
 
l705.6%
 
c514.1%
 
t463.7%
 
p463.7%
 
q443.5%
 
s423.3%
 
d342.7%
 
m262.1%
 
o221.8%
 
N171.4%
 
r161.3%
 
S151.2%
 
g141.1%
 
A121.0%
 
b121.0%
 
h121.0%
 
v40.3%
 
D20.2%
 
j20.2%
 

Most frequent None characters

ValueCountFrequency (%) 
ó15100.0%
 

algoritmo_pds
Categorical

Distinct3
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size496.0 B
Nunca_estudei_e_nunca_apliquei
25 
Só_estudei_nunca_apliquei
18 
Apliquei_em_algum_trabalho
ValueCountFrequency (%) 
Nunca_estudei_e_nunca_apliquei2554.3%
 
Só_estudei_nunca_apliquei1839.1%
 
Apliquei_em_algum_trabalho36.5%
 
2020-12-02T15:19:44.278924image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:44.360003image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:44.456020image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length30
Mean length27.7826087
Min length25

Overview of Unicode Properties

Unique unicode characters23
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_16312.8%
 
e16012.5%
 
u16012.5%
 
i13510.6%
 
a1209.4%
 
n1118.7%
 
c685.3%
 
l524.1%
 
t463.6%
 
p463.6%
 
q463.6%
 
s433.4%
 
d433.4%
 
N252.0%
 
S181.4%
 
ó181.4%
 
m60.5%
 
A30.2%
 
g30.2%
 
r30.2%
 
b30.2%
 
h30.2%
 
o30.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter106983.6%
 
Connector Punctuation16312.8%
 
Uppercase Letter463.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N2554.3%
 
S1839.1%
 
A36.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e16015.0%
 
u16015.0%
 
i13512.6%
 
a12011.2%
 
n11110.4%
 
c686.4%
 
l524.9%
 
t464.3%
 
p464.3%
 
q464.3%
 
s434.0%
 
d434.0%
 
ó181.7%
 
m60.6%
 
g30.3%
 
r30.3%
 
b30.3%
 
h30.3%
 
o30.3%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_163100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin111587.2%
 
Common16312.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e16014.3%
 
u16014.3%
 
i13512.1%
 
a12010.8%
 
n11110.0%
 
c686.1%
 
l524.7%
 
t464.1%
 
p464.1%
 
q464.1%
 
s433.9%
 
d433.9%
 
N252.2%
 
S181.6%
 
ó181.6%
 
m60.5%
 
A30.3%
 
g30.3%
 
r30.3%
 
b30.3%
 
h30.3%
 
o30.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
_163100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII126098.6%
 
None181.4%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_16312.9%
 
e16012.7%
 
u16012.7%
 
i13510.7%
 
a1209.5%
 
n1118.8%
 
c685.4%
 
l524.1%
 
t463.7%
 
p463.7%
 
q463.7%
 
s433.4%
 
d433.4%
 
N252.0%
 
S181.4%
 
m60.5%
 
A30.2%
 
g30.2%
 
r30.2%
 
b30.2%
 
h30.2%
 
o30.2%
 

Most frequent None characters

ValueCountFrequency (%) 
ó18100.0%
 
Distinct3
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size496.0 B
Nunca_estudei_e_nunca_apliquei
22 
Só_estudei_nunca_apliquei
21 
Apliquei_em_algum_trabalho
ValueCountFrequency (%) 
Nunca_estudei_e_nunca_apliquei2247.8%
 
Só_estudei_nunca_apliquei2145.7%
 
Apliquei_em_algum_trabalho36.5%
 
2020-12-02T15:19:44.601824image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:44.714440image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:44.816800image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length26
Mean length27.45652174
Min length25

Overview of Unicode Properties

Unique unicode characters23
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_16012.7%
 
u15712.4%
 
e15712.4%
 
i13510.7%
 
a1179.3%
 
n1088.6%
 
c655.1%
 
l524.1%
 
t463.6%
 
p463.6%
 
q463.6%
 
s433.4%
 
d433.4%
 
N221.7%
 
S211.7%
 
ó211.7%
 
m60.5%
 
A30.2%
 
g30.2%
 
r30.2%
 
b30.2%
 
h30.2%
 
o30.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter105783.7%
 
Connector Punctuation16012.7%
 
Uppercase Letter463.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N2247.8%
 
S2145.7%
 
A36.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
u15714.9%
 
e15714.9%
 
i13512.8%
 
a11711.1%
 
n10810.2%
 
c656.1%
 
l524.9%
 
t464.4%
 
p464.4%
 
q464.4%
 
s434.1%
 
d434.1%
 
ó212.0%
 
m60.6%
 
g30.3%
 
r30.3%
 
b30.3%
 
h30.3%
 
o30.3%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_160100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin110387.3%
 
Common16012.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
u15714.2%
 
e15714.2%
 
i13512.2%
 
a11710.6%
 
n1089.8%
 
c655.9%
 
l524.7%
 
t464.2%
 
p464.2%
 
q464.2%
 
s433.9%
 
d433.9%
 
N222.0%
 
S211.9%
 
ó211.9%
 
m60.5%
 
A30.3%
 
g30.3%
 
r30.3%
 
b30.3%
 
h30.3%
 
o30.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
_160100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII124298.3%
 
None211.7%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_16012.9%
 
u15712.6%
 
e15712.6%
 
i13510.9%
 
a1179.4%
 
n1088.7%
 
c655.2%
 
l524.2%
 
t463.7%
 
p463.7%
 
q463.7%
 
s433.5%
 
d433.5%
 
N221.8%
 
S211.7%
 
m60.5%
 
A30.2%
 
g30.2%
 
r30.2%
 
b30.2%
 
h30.2%
 
o30.2%
 

Most frequent None characters

ValueCountFrequency (%) 
ó21100.0%
 
Distinct3
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size496.0 B
Nunca_estudei_e_nunca_apliquei
29 
Só_estudei_nunca_apliquei
13 
Apliquei_em_algum_trabalho
ValueCountFrequency (%) 
Nunca_estudei_e_nunca_apliquei2963.0%
 
Só_estudei_nunca_apliquei1328.3%
 
Apliquei_em_algum_trabalho48.7%
 
2020-12-02T15:19:44.942295image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-02T15:19:45.024596image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:45.122464image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length30
Mean length28.23913043
Min length25

Overview of Unicode Properties

Unique unicode characters23
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_16712.9%
 
e16312.5%
 
u16312.5%
 
i13410.3%
 
a1259.6%
 
n1138.7%
 
c715.5%
 
l544.2%
 
t463.5%
 
p463.5%
 
q463.5%
 
s423.2%
 
d423.2%
 
N292.2%
 
S131.0%
 
ó131.0%
 
m80.6%
 
A40.3%
 
g40.3%
 
r40.3%
 
b40.3%
 
h40.3%
 
o40.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter108683.6%
 
Connector Punctuation16712.9%
 
Uppercase Letter463.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N2963.0%
 
S1328.3%
 
A48.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e16315.0%
 
u16315.0%
 
i13412.3%
 
a12511.5%
 
n11310.4%
 
c716.5%
 
l545.0%
 
t464.2%
 
p464.2%
 
q464.2%
 
s423.9%
 
d423.9%
 
ó131.2%
 
m80.7%
 
g40.4%
 
r40.4%
 
b40.4%
 
h40.4%
 
o40.4%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_167100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin113287.1%
 
Common16712.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e16314.4%
 
u16314.4%
 
i13411.8%
 
a12511.0%
 
n11310.0%
 
c716.3%
 
l544.8%
 
t464.1%
 
p464.1%
 
q464.1%
 
s423.7%
 
d423.7%
 
N292.6%
 
S131.1%
 
ó131.1%
 
m80.7%
 
A40.4%
 
g40.4%
 
r40.4%
 
b40.4%
 
h40.4%
 
o40.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
_167100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII128699.0%
 
None131.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_16713.0%
 
e16312.7%
 
u16312.7%
 
i13410.4%
 
a1259.7%
 
n1138.8%
 
c715.5%
 
l544.2%
 
t463.6%
 
p463.6%
 
q463.6%
 
s423.3%
 
d423.3%
 
N292.3%
 
S131.0%
 
m80.6%
 
A40.3%
 
g40.3%
 
r40.3%
 
b40.3%
 
h40.3%
 
o40.3%
 

Most frequent None characters

ValueCountFrequency (%) 
ó13100.0%
 
Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Nunca_estudei_e_nunca_apliquei
33 
Só_estudei_nunca_apliquei
Apliquei_em_algum_trabalho
 
3
Desenvolvi_grandes_projetos_com_isso
 
1
ValueCountFrequency (%) 
Nunca_estudei_e_nunca_apliquei3371.7%
 
Só_estudei_nunca_apliquei919.6%
 
Apliquei_em_algum_trabalho36.5%
 
Desenvolvi_grandes_projetos_com_isso12.2%
 
2020-12-02T15:19:45.254045image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)2.2%
2020-12-02T15:19:45.352530image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:45.474897image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length30
Mean length28.89130435
Min length25

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_17212.9%
 
e16912.7%
 
u16512.4%
 
i13410.1%
 
a1279.6%
 
n1199.0%
 
c765.7%
 
l523.9%
 
s473.5%
 
t463.5%
 
p463.5%
 
q453.4%
 
d433.2%
 
N332.5%
 
S90.7%
 
ó90.7%
 
o80.6%
 
m70.5%
 
r50.4%
 
g40.3%
 
A30.2%
 
b30.2%
 
h30.2%
 
v20.2%
 
D10.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter111183.6%
 
Connector Punctuation17212.9%
 
Uppercase Letter463.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N3371.7%
 
S919.6%
 
A36.5%
 
D12.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e16915.2%
 
u16514.9%
 
i13412.1%
 
a12711.4%
 
n11910.7%
 
c766.8%
 
l524.7%
 
s474.2%
 
t464.1%
 
p464.1%
 
q454.1%
 
d433.9%
 
ó90.8%
 
o80.7%
 
m70.6%
 
r50.5%
 
g40.4%
 
b30.3%
 
h30.3%
 
v20.2%
 
j10.1%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_172100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin115787.1%
 
Common17212.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e16914.6%
 
u16514.3%
 
i13411.6%
 
a12711.0%
 
n11910.3%
 
c766.6%
 
l524.5%
 
s474.1%
 
t464.0%
 
p464.0%
 
q453.9%
 
d433.7%
 
N332.9%
 
S90.8%
 
ó90.8%
 
o80.7%
 
m70.6%
 
r50.4%
 
g40.3%
 
A30.3%
 
b30.3%
 
h30.3%
 
v20.2%
 
D10.1%
 
j10.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
_172100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII132099.3%
 
None90.7%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_17213.0%
 
e16912.8%
 
u16512.5%
 
i13410.2%
 
a1279.6%
 
n1199.0%
 
c765.8%
 
l523.9%
 
s473.6%
 
t463.5%
 
p463.5%
 
q453.4%
 
d433.3%
 
N332.5%
 
S90.7%
 
o80.6%
 
m70.5%
 
r50.4%
 
g40.3%
 
A30.2%
 
b30.2%
 
h30.2%
 
v20.2%
 
D10.1%
 
j10.1%
 

Most frequent None characters

ValueCountFrequency (%) 
ó9100.0%
 

algoritmo_robotica
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size496.0 B
Nunca_estudei_e_nunca_apliquei
34 
Só_estudei_nunca_apliquei
Apliquei_em_algum_trabalho
 
2
Desenvolvi_grandes_projetos_com_isso
 
1
ValueCountFrequency (%) 
Nunca_estudei_e_nunca_apliquei3473.9%
 
Só_estudei_nunca_apliquei919.6%
 
Apliquei_em_algum_trabalho24.3%
 
Desenvolvi_grandes_projetos_com_isso12.2%
 
2020-12-02T15:19:45.618926image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)2.2%
2020-12-02T15:19:45.715061image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:45.831468image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length30
Mean length28.97826087
Min length25

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
_17313.0%
 
e17112.8%
 
u16712.5%
 
i13510.1%
 
a1279.5%
 
n1229.2%
 
c785.9%
 
l503.8%
 
s483.6%
 
t463.5%
 
p463.5%
 
q453.4%
 
d443.3%
 
N342.6%
 
S90.7%
 
ó90.7%
 
o70.5%
 
m50.4%
 
r40.3%
 
g30.2%
 
A20.2%
 
b20.2%
 
h20.2%
 
v20.2%
 
D10.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter111483.6%
 
Connector Punctuation17313.0%
 
Uppercase Letter463.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N3473.9%
 
S919.6%
 
A24.3%
 
D12.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e17115.4%
 
u16715.0%
 
i13512.1%
 
a12711.4%
 
n12211.0%
 
c787.0%
 
l504.5%
 
s484.3%
 
t464.1%
 
p464.1%
 
q454.0%
 
d443.9%
 
ó90.8%
 
o70.6%
 
m50.4%
 
r40.4%
 
g30.3%
 
b20.2%
 
h20.2%
 
v20.2%
 
j10.1%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_173100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin116087.0%
 
Common17313.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e17114.7%
 
u16714.4%
 
i13511.6%
 
a12710.9%
 
n12210.5%
 
c786.7%
 
l504.3%
 
s484.1%
 
t464.0%
 
p464.0%
 
q453.9%
 
d443.8%
 
N342.9%
 
S90.8%
 
ó90.8%
 
o70.6%
 
m50.4%
 
r40.3%
 
g30.3%
 
A20.2%
 
b20.2%
 
h20.2%
 
v20.2%
 
D10.1%
 
j10.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
_173100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII132499.3%
 
None90.7%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
_17313.1%
 
e17112.9%
 
u16712.6%
 
i13510.2%
 
a1279.6%
 
n1229.2%
 
c785.9%
 
l503.8%
 
s483.6%
 
t463.5%
 
p463.5%
 
q453.4%
 
d443.3%
 
N342.6%
 
S90.7%
 
o70.5%
 
m50.4%
 
r40.3%
 
g30.2%
 
A20.2%
 
b20.2%
 
h20.2%
 
v20.2%
 
D10.1%
 
j10.1%
 

Most frequent None characters

ValueCountFrequency (%) 
ó9100.0%
 

nota_trabalho
Real number (ℝ≥0)

Distinct7
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.927173913
Minimum0.65
Maximum1
Zeros0
Zeros (%)0.0%
Memory size496.0 B
2020-12-02T15:19:45.956683image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.65
5-th percentile0.7
Q10.875
median1
Q31
95-th percentile1
Maximum1
Range0.35
Interquartile range (IQR)0.125

Descriptive statistics

Standard deviation0.1006680583
Coefficient of variation (CV)0.1085751626
Kurtosis1.46485901
Mean0.927173913
Median Absolute Deviation (MAD)0
Skewness-1.449768014
Sum42.65
Variance0.01013405797
MonotocityNot monotonic
2020-12-02T15:19:46.125239image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
12554.3%
 
0.8751226.1%
 
0.924.3%
 
0.6524.3%
 
0.9524.3%
 
0.724.3%
 
0.7512.2%
 
ValueCountFrequency (%) 
0.6524.3%
 
0.724.3%
 
0.7512.2%
 
0.8751226.1%
 
0.924.3%
 
0.9524.3%
 
12554.3%
 
ValueCountFrequency (%) 
12554.3%
 
0.9524.3%
 
0.924.3%
 
0.8751226.1%
 
0.7512.2%
 
0.724.3%
 
0.6524.3%
 

nota_ingles
Real number (ℝ≥0)

Distinct15
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7184782609
Minimum0.45
Maximum1
Zeros0
Zeros (%)0.0%
Memory size496.0 B
2020-12-02T15:19:46.258491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.45
5-th percentile0.45
Q10.53125
median0.75
Q30.8875
95-th percentile0.95
Maximum1
Range0.55
Interquartile range (IQR)0.35625

Descriptive statistics

Standard deviation0.1893288426
Coefficient of variation (CV)0.2635136689
Kurtosis-1.406843625
Mean0.7184782609
Median Absolute Deviation (MAD)0.15
Skewness-0.2224795685
Sum33.05
Variance0.03584541063
MonotocityNot monotonic
2020-12-02T15:19:46.399373image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%) 
0.45817.4%
 
0.95613.0%
 
0.75613.0%
 
0.8510.9%
 
0.636.5%
 
0.8536.5%
 
0.936.5%
 
0.47536.5%
 
0.5524.3%
 
124.3%
 
0.8512.2%
 
0.912.2%
 
0.72512.2%
 
0.67512.2%
 
0.52512.2%
 
ValueCountFrequency (%) 
0.45817.4%
 
0.47536.5%
 
0.52512.2%
 
0.5524.3%
 
0.636.5%
 
0.67512.2%
 
0.72512.2%
 
0.75613.0%
 
0.8510.9%
 
0.8512.2%
 
ValueCountFrequency (%) 
124.3%
 
0.95613.0%
 
0.936.5%
 
0.912.2%
 
0.8536.5%
 
0.8512.2%
 
0.8510.9%
 
0.75613.0%
 
0.72512.2%
 
0.67512.2%
 

nota_profissional
Real number (ℝ≥0)

Distinct16
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7970108696
Minimum0.6625
Maximum1
Zeros0
Zeros (%)0.0%
Memory size496.0 B
2020-12-02T15:19:46.535890image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.6625
5-th percentile0.7
Q10.740625
median0.78125
Q30.8375
95-th percentile0.975
Maximum1
Range0.3375
Interquartile range (IQR)0.096875

Descriptive statistics

Standard deviation0.08037173972
Coefficient of variation (CV)0.10084146
Kurtosis0.7313226759
Mean0.7970108696
Median Absolute Deviation (MAD)0.05
Skewness0.9002435117
Sum36.6625
Variance0.006459616546
MonotocityNot monotonic
2020-12-02T15:19:46.671397image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
0.8125715.2%
 
0.875510.9%
 
0.762548.7%
 
0.77548.7%
 
0.837536.5%
 
0.787536.5%
 
0.72536.5%
 
0.736.5%
 
136.5%
 
0.7536.5%
 
0.924.3%
 
0.712524.3%
 
0.662512.2%
 
0.737512.2%
 
0.737512.2%
 
0.687512.2%
 
ValueCountFrequency (%) 
0.662512.2%
 
0.687512.2%
 
0.736.5%
 
0.712524.3%
 
0.72536.5%
 
0.737512.2%
 
0.737512.2%
 
0.7536.5%
 
0.762548.7%
 
0.77548.7%
 
ValueCountFrequency (%) 
136.5%
 
0.924.3%
 
0.875510.9%
 
0.837536.5%
 
0.8125715.2%
 
0.787536.5%
 
0.77548.7%
 
0.762548.7%
 
0.7536.5%
 
0.737512.2%
 

nota_ferramentas
Real number (ℝ≥0)

Distinct38
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6256631118
Minimum0.5
Maximum0.8893081761
Zeros0
Zeros (%)0.0%
Memory size496.0 B
2020-12-02T15:19:46.844593image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5062893082
Q10.558490566
median0.6088050314
Q30.6621069182
95-th percentile0.813836478
Maximum0.8893081761
Range0.3893081761
Interquartile range (IQR)0.1036163522

Descriptive statistics

Standard deviation0.09040493665
Coefficient of variation (CV)0.1444945929
Kurtosis0.8429884705
Mean0.6256631118
Median Absolute Deviation (MAD)0.05314465409
Skewness1.003034925
Sum28.78050314
Variance0.00817305257
MonotocityNot monotonic
2020-12-02T15:19:47.039967image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%) 
0.536.5%
 
0.542767295636.5%
 
0.610691823936.5%
 
0.545283018924.3%
 
0.655974842824.3%
 
0.664150943412.2%
 
0.600628930812.2%
 
0.710062893112.2%
 
0.697484276712.2%
 
0.725786163512.2%
 
0.601257861612.2%
 
0.580503144712.2%
 
0.60691823912.2%
 
0.889308176112.2%
 
0.59308176112.2%
 
0.531446540912.2%
 
0.525157232712.2%
 
0.596226415112.2%
 
0.618238993712.2%
 
0.613207547212.2%
 
0.835220125812.2%
 
0.71383647812.2%
 
0.822641509412.2%
 
0.59308176112.2%
 
0.653459119512.2%
 
Other values (13)1328.3%
 
ValueCountFrequency (%) 
0.536.5%
 
0.525157232712.2%
 
0.531446540912.2%
 
0.537735849112.2%
 
0.542767295636.5%
 
0.545283018924.3%
 
0.557861635212.2%
 
0.560377358512.2%
 
0.567924528312.2%
 
0.570440251612.2%
 
ValueCountFrequency (%) 
0.889308176112.2%
 
0.835220125812.2%
 
0.822641509412.2%
 
0.787421383612.2%
 
0.725786163512.2%
 
0.720125786212.2%
 
0.71383647812.2%
 
0.711949685512.2%
 
0.711320754712.2%
 
0.710062893112.2%
 

nota_algoritmos
Real number (ℝ≥0)

Distinct39
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3745022349
Minimum0.1
Maximum0.7514018692
Zeros0
Zeros (%)0.0%
Memory size496.0 B
2020-12-02T15:19:47.189485image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.2037383178
median0.3635514019
Q30.5105140187
95-th percentile0.6988317757
Maximum0.7514018692
Range0.6514018692
Interquartile range (IQR)0.3067757009

Descriptive statistics

Standard deviation0.1904483101
Coefficient of variation (CV)0.5085371792
Kurtosis-0.970939287
Mean0.3745022349
Median Absolute Deviation (MAD)0.1570093458
Skewness0.2900898021
Sum17.2271028
Variance0.03627055883
MonotocityNot monotonic
2020-12-02T15:19:47.331223image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%) 
0.148.7%
 
0.156074766424.3%
 
0.163551401924.3%
 
0.357943925224.3%
 
0.48504672924.3%
 
0.699065420612.2%
 
0.667289719612.2%
 
0.588785046712.2%
 
0.350467289712.2%
 
0.221495327112.2%
 
0.401869158912.2%
 
0.193457943912.2%
 
0.227102803712.2%
 
0.369158878512.2%
 
0.728971962612.2%
 
0.404672897212.2%
 
0.404672897212.2%
 
0.443925233612.2%
 
0.563551401912.2%
 
0.555140186912.2%
 
0.212149532712.2%
 
0.609345794412.2%
 
0.698130841112.2%
 
0.511214953312.2%
 
0.592523364512.2%
 
Other values (14)1430.4%
 
ValueCountFrequency (%) 
0.148.7%
 
0.156074766424.3%
 
0.163551401924.3%
 
0.165420560712.2%
 
0.174766355112.2%
 
0.193457943912.2%
 
0.200934579412.2%
 
0.212149532712.2%
 
0.221495327112.2%
 
0.227102803712.2%
 
ValueCountFrequency (%) 
0.751401869212.2%
 
0.728971962612.2%
 
0.699065420612.2%
 
0.698130841112.2%
 
0.667289719612.2%
 
0.609345794412.2%
 
0.592523364512.2%
 
0.588785046712.2%
 
0.563551401912.2%
 
0.556074766412.2%
 

nota_Final
Real number (ℝ≥0)

Distinct45
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.688565678
Minimum0.53
Maximum0.8836560277
Zeros0
Zeros (%)0.0%
Memory size496.0 B
2020-12-02T15:19:47.527654image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.53
5-th percentile0.5562578616
Q10.6155138717
median0.6765979692
Q30.7484771572
95-th percentile0.8483990551
Maximum0.8836560277
Range0.3536560277
Interquartile range (IQR)0.1329632854

Descriptive statistics

Standard deviation0.09099675776
Coefficient of variation (CV)0.1321540714
Kurtosis-0.5675805849
Mean0.688565678
Median Absolute Deviation (MAD)0.07079659672
Skewness0.3573023943
Sum31.67402119
Variance0.008280409923
MonotocityNot monotonic
2020-12-02T15:19:47.727601image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%) 
0.690704314324.3%
 
0.592558925512.2%
 
0.74870187512.2%
 
0.771334861612.2%
 
0.707779492212.2%
 
0.606209810112.2%
 
0.854808675712.2%
 
0.5312.2%
 
0.5512.2%
 
0.821179098312.2%
 
0.640577058712.2%
 
0.729886410412.2%
 
0.676869305812.2%
 
0.653714953312.2%
 
0.546263739512.2%
 
0.796173808312.2%
 
0.783417974512.2%
 
0.734910803512.2%
 
0.587594780512.2%
 
0.676326632612.2%
 
0.883656027712.2%
 
0.667068271312.2%
 
0.829170193412.2%
 
0.732035208412.2%
 
0.579886116512.2%
 
Other values (20)2043.5%
 
ValueCountFrequency (%) 
0.5312.2%
 
0.546263739512.2%
 
0.5512.2%
 
0.575031446512.2%
 
0.579886116512.2%
 
0.582852524512.2%
 
0.587594780512.2%
 
0.592558925512.2%
 
0.594776935312.2%
 
0.598687621212.2%
 
ValueCountFrequency (%) 
0.883656027712.2%
 
0.877297360812.2%
 
0.854808675712.2%
 
0.829170193412.2%
 
0.821179098312.2%
 
0.820021160312.2%
 
0.796173808312.2%
 
0.783417974512.2%
 
0.772026391612.2%
 
0.771334861612.2%
 

aprovado
Boolean

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size496.0 B
False
28 
True
18 
ValueCountFrequency (%) 
False2860.9%
 
True1839.1%
 
2020-12-02T15:19:47.870351image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Interactions

2020-12-02T15:19:10.557851image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:10.706837image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:10.851858image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:10.982696image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:11.094536image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:11.210086image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:11.337471image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:11.453503image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:11.573675image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:11.711403image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:11.850841image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:11.999662image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:12.153308image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:12.285311image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:12.427796image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:12.560910image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:12.714874image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:12.843285image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:12.978467image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:13.103921image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:13.225156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:13.352737image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:13.495313image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:13.629254image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:13.754687image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:13.991183image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:14.114335image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:14.234327image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:14.345348image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:14.483809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:14.616165image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:14.758399image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:14.884197image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:15.003268image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:15.155366image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:15.296016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:15.414245image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:15.576242image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:15.711272image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:15.840630image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:15.972145image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:16.118297image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:16.255471image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:16.383113image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:16.524971image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:16.702150image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:16.881495image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:17.014516image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:17.148032image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:17.267247image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:17.384304image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:17.503816image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:17.621479image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:17.743351image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:17.869441image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:17.989196image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:18.105682image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:18.216485image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:18.340864image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:18.635616image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:18.750234image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:18.875970image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:19.009398image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:19.126868image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-02T15:19:47.963569image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-02T15:19:48.176749image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-02T15:19:48.356004image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-02T15:19:48.669487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-02T15:19:50.325538image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-02T15:19:19.603949image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-02T15:19:23.758605image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

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122Balon Greyjoy23TrueTrue30hrestritoBBB0mesesgraduacaoCTrue2020/22/30NãoNãoJá_ouvi_FalarNunca_nem_viNunca_nem_viJá_ouvi_FalarNunca_nem_viJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viNunca_nem_viNunca_nem_viJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viNunca_nem_viNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliquei0.8750.4500.72500.5000000.1000000.530000False
233Bran Stark24TrueTrue30hfullFFA0mesesgraduacaoBTrue2021/12/30ConcluidoConcluidoTenho_domínioJá_ouvi_FalarJá_trabalheiTenho_domínioTenho_domínioTenho_domínioJá_trabalheiJá_ouvi_FalarJá_trabalheiJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_trabalheiJá_ouvi_FalarTenho_domínioTenho_domínioJá_trabalheiJá_ouvi_FalarTenho_domínioTenho_domínioTenho_domínioTenho_domínioJá_trabalheiJá_ouvi_FalarTenho_domínioDesenvolvi_grandes_projetos_com_issoDesenvolvi_grandes_projetos_com_issoSó_estudei_nunca_apliqueiDesenvolvi_grandes_projetos_com_issoApliquei_em_algum_trabalhoSó_estudei_nunca_apliqueiDesenvolvi_grandes_projetos_com_issoDesenvolvi_grandes_projetos_com_issoSó_estudei_nunca_apliqueiApliquei_em_algum_trabalhoSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiSó_estudei_nunca_apliquei1.0000.9000.90000.8893080.7289720.883656True
355Bronn22FalseFalse20hrestritoAIF6mesesgraduacaoBTrue2022/11/30NãoNãoJá_trabalheiJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliquei0.6500.8500.75000.5251570.1000000.575031False
466Catelyn Stark20TrueTrue30hfullIIB0mesesgraduacaoATrue2022/21/30NãoNãoTenho_domínioJá_ouvi_FalarJá_trabalheiJá_ouvi_FalarNunca_nem_viJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viNunca_nem_viJá_ouvi_FalarNunca_nem_viJá_trabalheiNunca_nem_viTenho_domínioJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viJá_trabalheiJá_ouvi_FalarNunca_nem_viNunca_nem_viNunca_nem_viApliquei_em_algum_trabalhoSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiApliquei_em_algum_trabalhoApliquei_em_algum_trabalhoSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiSó_estudei_nunca_apliquei1.0000.4750.76250.5962260.3691590.640577False
577Cersei Lannister20TrueTrue30hfullFAA0mesesgraduacaoBTrue2022/21/30ConcluidoNãoTenho_domínioJá_trabalheiJá_ouvi_FalarJá_trabalheiJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_trabalheiNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viJá_trabalheiJá_trabalheiJá_trabalheiJá_trabalheiJá_trabalheiJá_trabalheiJá_ouvi_FalarNunca_nem_viJá_trabalheiSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliqueiSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliquei1.0000.8500.78750.7100630.3018690.729886True
688Daario Naharis23TrueTrue30hrestritoFAI0mesesgraduacaoATrue2021/22/30NãoNãoJá_trabalheiJá_ouvi_FalarJá_trabalheiJá_trabalheiJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viJá_ouvi_FalarNunca_nem_viJá_trabalheiJá_trabalheiJá_trabalheiJá_trabalheiJá_ouvi_FalarJá_trabalheiJá_ouvi_FalarNunca_nem_viNunca_nem_viSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliquei0.8750.8500.81250.6459120.2009350.676869False
71010Davos Seaworth23TrueTrue30hfullAAA0mesesgraduacaoATrue2022/12/30NãoNãoTenho_domínioTenho_domínioTenho_domínioJá_trabalheiJá_ouvi_FalarJá_ouvi_FalarJá_trabalheiJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viJá_ouvi_FalarJá_trabalheiJá_ouvi_FalarJá_trabalheiJá_trabalheiJá_trabalheiJá_trabalheiJá_trabalheiJá_trabalheiTenho_domínioTenho_domínioTenho_domínioNunca_nem_viTenho_domínioDesenvolvi_grandes_projetos_com_issoApliquei_em_algum_trabalhoApliquei_em_algum_trabalhoApliquei_em_algum_trabalhoApliquei_em_algum_trabalhoApliquei_em_algum_trabalhoApliquei_em_algum_trabalhoApliquei_em_algum_trabalhoSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiSó_estudei_nunca_apliquei1.0000.8000.81250.8352200.6981310.829170True
81111Doran Martell23TrueTrue30hfullFFF6mesesgraduacaoBTrue2022/12/31NãoNãoJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viJá_ouvi_FalarJá_ouvi_FalarTenho_domínioJá_ouvi_FalarNunca_nem_viNunca_nem_viSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliqueiSó_estudei_nunca_apliqueiSó_estudei_nunca_apliquei1.0001.0000.83750.5314470.3056070.734911True
91212Eddard Ned Stark23TrueTrue30hrestritoFAF6mesesgraduacaoBTrue2021/12/30NãoNãoJá_trabalheiTenho_domínioTenho_domínioJá_trabalheiJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viJá_ouvi_FalarNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viJá_ouvi_FalarJá_ouvi_FalarTenho_domínioJá_trabalheiNunca_nem_viJá_ouvi_FalarJá_trabalheiTenho_domínioJá_trabalheiNunca_nem_viJá_ouvi_FalarApliquei_em_algum_trabalhoApliquei_em_algum_trabalhoSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiApliquei_em_algum_trabalhoApliquei_em_algum_trabalhoNunca_estudei_e_nunca_apliqueiSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliqueiSó_estudei_nunca_apliqueiApliquei_em_algum_trabalhoApliquei_em_algum_trabalhoNunca_estudei_e_nunca_apliquei0.8750.9500.77500.6641510.5925230.771335True

Last rows

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394343Stannis Baratheon39TrueTrue30hfullAAA0mesesgraduacaoAFalse2022/20/30NãoNãoJá_trabalheiJá_trabalheiJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viJá_ouvi_FalarNunca_nem_viJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viJá_ouvi_FalarNunca_nem_viJá_trabalheiJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliquei1.0000.8000.70000.5679250.3579440.685174False
404444Theon Greyjoy22TrueFalse20hrestritoFFF0mesesgraduacaoATrue2021/22/31ConcluidoFazendoTenho_domínioJá_trabalheiJá_trabalheiTenho_domínioJá_trabalheiJá_ouvi_FalarJá_trabalheiJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viJá_ouvi_FalarJá_trabalheiNunca_nem_viJá_ouvi_FalarNunca_nem_viJá_trabalheiJá_ouvi_FalarJá_ouvi_FalarJá_trabalheiJá_trabalheiJá_trabalheiJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarApliquei_em_algum_trabalhoApliquei_em_algum_trabalhoSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliqueiSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiApliquei_em_algum_trabalhoSó_estudei_nunca_apliqueiApliquei_em_algum_trabalhoSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliquei0.7000.9501.00000.7119500.5551400.783418True
414545Tommen Baratheon27TrueTrue30hfullFII0mesesgraduacaoCTrue2021/12/30NãoFazendoJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viJá_ouvi_FalarNunca_nem_viNunca_nem_viJá_ouvi_FalarNunca_nem_viNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliqueiSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliquei1.0000.8000.81250.5000000.1560750.653715False
424747Tywin Lannister24TrueTrue30hfullFAF12mesesgraduacaoATrue2021/22/30NãoFazendoTenho_domínioJá_ouvi_FalarJá_trabalheiJá_trabalheiJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viJá_ouvi_FalarNunca_nem_viNunca_nem_viJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viJá_trabalheiJá_trabalheiJá_trabalheiJá_ouvi_FalarJá_ouvi_FalarJá_trabalheiJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarApliquei_em_algum_trabalhoSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliqueiApliquei_em_algum_trabalhoApliquei_em_algum_trabalhoSó_estudei_nunca_apliqueiApliquei_em_algum_trabalhoApliquei_em_algum_trabalhoSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiSó_estudei_nunca_apliquei1.0000.9500.87500.6446540.5112150.796174True
434949Verme Cinzento24TrueTrue30hfullAII0mesesgraduacaoATrue2021/12/3>1NãoNãoJá_trabalheiJá_trabalheiJá_ouvi_FalarJá_trabalheiJá_trabalheiJá_trabalheiJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viNunca_nem_viJá_ouvi_FalarJá_ouvi_FalarJá_trabalheiJá_trabalheiJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_trabalheiJá_ouvi_FalarJá_trabalheiJá_trabalheiJá_ouvi_FalarJá_ouvi_FalarApliquei_em_algum_trabalhoSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiApliquei_em_algum_trabalhoApliquei_em_algum_trabalhoSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliqueiSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliquei1.0000.7500.87500.7138360.4046730.748702True
445050Viserys Targaryen24TrueTrue30hfullFFA12mesesgraduacaoBTrue2021/21/30NãoNãoTenho_domínioJá_ouvi_FalarJá_trabalheiJá_trabalheiJá_trabalheiJá_ouvi_FalarJá_trabalheiJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_trabalheiJá_ouvi_FalarTenho_domínioTenho_domínioJá_ouvi_FalarJá_ouvi_FalarJá_trabalheiTenho_domínioTenho_domínioTenho_domínioJá_trabalheiNunca_nem_viJá_trabalheiDesenvolvi_grandes_projetos_com_issoApliquei_em_algum_trabalhoSó_estudei_nunca_apliqueiDesenvolvi_grandes_projetos_com_issoDesenvolvi_grandes_projetos_com_issoDesenvolvi_grandes_projetos_com_issoApliquei_em_algum_trabalhoApliquei_em_algum_trabalhoApliquei_em_algum_trabalhoSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliquei1.0000.9500.75000.8226420.7514020.854809True
455151Walder Frey26TrueTrue30hrestritoIBB0mesesgraduacaoBTrue2022/12/30NãoNãoJá_trabalheiJá_ouvi_FalarJá_trabalheiJá_trabalheiJá_trabalheiJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viNunca_nem_viJá_trabalheiJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarJá_trabalheiJá_ouvi_FalarJá_ouvi_FalarJá_ouvi_FalarSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiSó_estudei_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliqueiNunca_estudei_e_nunca_apliquei0.8750.4500.77500.6056600.2682240.594777False